このレポートは、原稿"Highly Sensitive Adolescent Benefits in Positive School Transitions"の結果をまとめたものである。結果の構成は以下のとおり。
#前処理に必要なパッケージ読み込み
library(tidyverse)
## -- Attaching packages ---------------------------------------------------------------- tidyverse 1.2.1 --
## √ ggplot2 2.2.1 √ purrr 0.2.5
## √ tibble 1.4.2 √ dplyr 0.7.6
## √ tidyr 0.8.1 √ stringr 1.3.0
## √ readr 1.1.1 √ forcats 0.3.0
## -- Conflicts ------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
#各時点のローデータ読み込み
DataSourceT1 <- read_csv("Time1_rawdata.csv", na = c(".", "")) #Time1のローデータ読み込み
## Parsed with column specification:
## cols(
## .default = col_integer()
## )
## See spec(...) for full column specifications.
DataSourceT2 <- read_csv("Time2_rawdata.csv", na = c(".", "")) #Time2のローデータ読み込み
## Parsed with column specification:
## cols(
## .default = col_integer()
## )
## See spec(...) for full column specifications.
head(DataSourceT1) #先頭6行確認
head(DataSourceT2) #先頭6行確認
#各時点のローデータの結合
DataSource <- full_join(DataSourceT1, DataSourceT2, by = "ID", na = c(".", ""))
#full_join(データ1, データ2, by = 共通のキー変数名)
head(DataSource) #先頭6行確認
tail(DataSource) #末尾6行確認
names(DataSource) #変数名確認
## [1] "ID" "gardian_gender_T1" "gardian_age_T1"
## [4] "prefecture_T1" "area_T1" "married_T1"
## [7] "familyincome_T1" "pinincome_T1" "job_T1"
## [10] "child_gender_T1" "hsc1_T1" "hsc2_T1"
## [13] "hsc3_T1" "hsc4_T1" "hsc5_T1"
## [16] "hsc6_T1" "hsc7_T1" "hsc8_T1"
## [19] "hsc9_T1" "hsc10_T1" "hsc11_T1"
## [22] "hsc12_T1" "health1_T1" "health2_T1"
## [25] "health3_T1" "health4_T1" "health5_T1"
## [28] "panas1_T1" "panas2_T1" "panas3_T1"
## [31] "panas4_T1" "panas5_T1" "panas6_T1"
## [34] "panas7_T1" "panas8_T1" "panas9_T1"
## [37] "panas10_T1" "panas11_T1" "panas12_T1"
## [40] "panas13_T1" "panas14_T1" "panas15_T1"
## [43] "panas16_T1" "gardian_gender_T2" "gardian_age_T2"
## [46] "prefecture_T2" "area_T2" "married_T2"
## [49] "familyincome_T2" "pinincome_T2" "job_T2"
## [52] "child_gender_T2" "environment1_T2" "environment2_T2"
## [55] "environment3_T2" "environment4_T2" "environment5_T2"
## [58] "environment6_T2" "environment7_T2" "environment8_T2"
## [61] "environment9_T2" "environment10_T2" "environment11_T2"
## [64] "hsc1_T2" "hsc2_T2" "hsc3_T2"
## [67] "hsc4_T2" "hsc5_T2" "hsc6_T2"
## [70] "hsc7_T2" "hsc8_T2" "hsc9_T2"
## [73] "hsc10_T2" "hsc11_T2" "hsc12_T2"
## [76] "health1_T2" "health2_T2" "health3_T2"
## [79] "health4_T2" "health5_T2" "bis1r_T2"
## [82] "bas1_T2" "bas2_T2" "bas3_T2"
## [85] "bas4_T2" "bis2_T2" "bas5_T2"
## [88] "bas6_T2" "bas7_T2" "bis3_T2"
## [91] "bas8_T2" "bas9_T2" "bis4_T2"
## [94] "bas10_T2" "bis5_T2" "bas11_T2"
## [97] "bas12_T2" "bis6r_T2" "bas13_T2"
## [100] "bis7_T2"
#合計得点の算出と列の追加:インプットデータ作成(# 参考として徳岡さんの資料参考http://rpubs.com/t_macya/333965)
InputData <- DataSource %>%
dplyr::mutate(bis1_T2 = 5 - bis1r_T2) %>% #bis1r_T2を逆転しbis1_T2という列を追加
dplyr::mutate(bis6_T2 = 5 - bis6r_T2) %>% #bis1r_T2を逆転しbis1_T2という列を追加
#HSCSの易興奮性得点算出(T1とT2)
dplyr::mutate(eoe_mean_T1 = (hsc8_T1 + hsc6_T1 + hsc4_T1 + hsc9_T1+ hsc12_T1)/5, na.rm = TRUE) %>% #T1
dplyr::mutate(eoe_mean_T2 = (hsc8_T2 + hsc6_T2 + hsc4_T2 + hsc9_T2+ hsc12_T2)/5, na.rm = TRUE) %>% #T2
#HSCSの低閾値得点算出(T1とT2)
dplyr::mutate(lst_mean_T1 = (hsc2_T1 + hsc11_T1)/2, na.rm = TRUE) %>% #T1
dplyr::mutate(lst_mean_T2 = (hsc2_T2 + hsc11_T2)/2, na.rm = TRUE) %>% #T2
#HSCSの美的感受性得点算出(T1とT2)
dplyr::mutate(aes_mean_T1 = (hsc5_T1 + hsc10_T1 + hsc1_T1 + hsc3_T1)/4, na.rm = TRUE) %>% #T1
dplyr::mutate(aes_mean_T2 = (hsc5_T2 + hsc10_T2 + hsc1_T2 + hsc3_T2)/4, na.rm = TRUE) %>% #T2
#HSCSの合計平均得点算出(T1とT2)
dplyr::mutate(hsc_mean_T1 = (eoe_mean_T1 + lst_mean_T1 + aes_mean_T1)/3, na.rm = TRUE) %>% #T1
dplyr::mutate(hsc_mean_T2 = (eoe_mean_T2 + lst_mean_T2 + aes_mean_T2)/3, na.rm = TRUE) %>% #T2
#精神的健康の合計得点算出(T1とT2)
dplyr::mutate(health_mean_T1 = (health1_T1 + health2_T1 + health2_T1 + health4_T1 + health5_T1)/5, na.rm = TRUE) %>% #T1
dplyr::mutate(health_mean_T2 = (health1_T2 + health2_T2 + health2_T2 + health4_T2 + health5_T2)/5, na.rm = TRUE) %>% #T2
#PANASのpositive得点算出(T1)
dplyr::mutate(positive_mean_T1 = (panas2_T1 + panas4_T1 + panas6_T1 + panas8_T1 + panas10_T1 + panas12_T1 + panas14_T1 + panas16_T1)/8, na.rm = TRUE) %>% #T1
#PANASのnegative得点算出(T1)
dplyr::mutate(negative_mean_T1 = (panas1_T1 + panas3_T1 + panas5_T1 + panas7_T1 + panas9_T1 + panas11_T1 + panas13_T1 + panas15_T1)/8, na.rm = TRUE) %>% #T1
#学校環境変化尺度の合計得点算出(T2)
dplyr::mutate(environment_mean_T2 = (environment1_T2 + environment2_T2 + environment3_T2 + environment4_T2 + environment5_T2 + environment6_T2 + environment7_T2 + environment8_T2 + environment9_T2 + environment10_T2 + environment11_T2)/11, na.rm = TRUE) %>% #T2
#BISの得点算出(T2)
dplyr::mutate(bis_mean_T2 = (bis1_T2 + bis2_T2 + bis3_T2 + bis4_T2 + bis5_T2 + bis6_T2 + bis7_T2)/7, na.rm = TRUE) %>% #T2
#BASの得点算出(T2)
dplyr::mutate(bas_mean_T2 = (bas1_T2 + bas2_T2 + bas3_T2 + bas4_T2 + bas5_T2 + bas6_T2 + bas7_T2 + bas8_T2 + bas9_T2 + bas10_T2 + bas11_T2 + bas12_T2 + bas13_T2)/7, na.rm = TRUE) #T2
#インプットデータの確認
head(InputData) #先頭6行確認
names(InputData) #変数名確認
## [1] "ID" "gardian_gender_T1" "gardian_age_T1"
## [4] "prefecture_T1" "area_T1" "married_T1"
## [7] "familyincome_T1" "pinincome_T1" "job_T1"
## [10] "child_gender_T1" "hsc1_T1" "hsc2_T1"
## [13] "hsc3_T1" "hsc4_T1" "hsc5_T1"
## [16] "hsc6_T1" "hsc7_T1" "hsc8_T1"
## [19] "hsc9_T1" "hsc10_T1" "hsc11_T1"
## [22] "hsc12_T1" "health1_T1" "health2_T1"
## [25] "health3_T1" "health4_T1" "health5_T1"
## [28] "panas1_T1" "panas2_T1" "panas3_T1"
## [31] "panas4_T1" "panas5_T1" "panas6_T1"
## [34] "panas7_T1" "panas8_T1" "panas9_T1"
## [37] "panas10_T1" "panas11_T1" "panas12_T1"
## [40] "panas13_T1" "panas14_T1" "panas15_T1"
## [43] "panas16_T1" "gardian_gender_T2" "gardian_age_T2"
## [46] "prefecture_T2" "area_T2" "married_T2"
## [49] "familyincome_T2" "pinincome_T2" "job_T2"
## [52] "child_gender_T2" "environment1_T2" "environment2_T2"
## [55] "environment3_T2" "environment4_T2" "environment5_T2"
## [58] "environment6_T2" "environment7_T2" "environment8_T2"
## [61] "environment9_T2" "environment10_T2" "environment11_T2"
## [64] "hsc1_T2" "hsc2_T2" "hsc3_T2"
## [67] "hsc4_T2" "hsc5_T2" "hsc6_T2"
## [70] "hsc7_T2" "hsc8_T2" "hsc9_T2"
## [73] "hsc10_T2" "hsc11_T2" "hsc12_T2"
## [76] "health1_T2" "health2_T2" "health3_T2"
## [79] "health4_T2" "health5_T2" "bis1r_T2"
## [82] "bas1_T2" "bas2_T2" "bas3_T2"
## [85] "bas4_T2" "bis2_T2" "bas5_T2"
## [88] "bas6_T2" "bas7_T2" "bis3_T2"
## [91] "bas8_T2" "bas9_T2" "bis4_T2"
## [94] "bas10_T2" "bis5_T2" "bas11_T2"
## [97] "bas12_T2" "bis6r_T2" "bas13_T2"
## [100] "bis7_T2" "bis1_T2" "bis6_T2"
## [103] "eoe_mean_T1" "na.rm" "eoe_mean_T2"
## [106] "lst_mean_T1" "lst_mean_T2" "aes_mean_T1"
## [109] "aes_mean_T2" "hsc_mean_T1" "hsc_mean_T2"
## [112] "health_mean_T1" "health_mean_T2" "positive_mean_T1"
## [115] "negative_mean_T1" "environment_mean_T2" "bis_mean_T2"
## [118] "bas_mean_T2"
楽に可視化したいなら以下のコードでGUI上のPlotlyタブをいじればOK! * library(ggplotgui) * ggplot_shiny(dataset = InputData)
#child_gender_T1の度数分布
child_gender_count_T1 <- dplyr::count(InputData, child_gender_T1)
knitr::kable(child_gender_count_T1) #テーブル化
| child_gender_T1 | n |
|---|---|
| 0 | 206 |
| 1 | 206 |
ggplot(data = InputData, mapping = aes(x = child_gender_T1, fill = factor(child_gender_T1))) + geom_bar() #視覚化
#child_gender_T2の度数分布
child_gender_count_T2 <- dplyr::count(InputData, child_gender_T2)
knitr::kable(child_gender_count_T2) #テーブル化
| child_gender_T2 | n |
|---|---|
| 0 | 174 |
| 1 | 170 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = child_gender_T2, fill = factor(child_gender_T2))) + geom_bar() #視覚化
#hsc1_T1の度数分布
hsc1_T1_count <- dplyr::count(InputData, hsc1_T1)
knitr::kable(hsc1_T1_count) #テーブル化
| hsc1_T1 | n |
|---|---|
| 1 | 8 |
| 2 | 30 |
| 3 | 57 |
| 4 | 147 |
| 5 | 114 |
| 6 | 41 |
| 7 | 15 |
ggplot(data = InputData, mapping = aes(x = hsc1_T1, fill = factor(hsc1_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc2_T1の度数分布
hsc2_T1_count <- dplyr::count(InputData, hsc2_T1)
knitr::kable(hsc2_T1_count) #テーブル化
| hsc2_T1 | n |
|---|---|
| 1 | 27 |
| 2 | 35 |
| 3 | 56 |
| 4 | 118 |
| 5 | 102 |
| 6 | 56 |
| 7 | 18 |
ggplot(data = InputData, mapping = aes(x = hsc2_T1, fill = factor(hsc2_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc3_T1の度数分布
hsc3_T1_count <- dplyr::count(InputData, hsc3_T1)
knitr::kable(hsc3_T1_count) #テーブル化
| hsc3_T1 | n |
|---|---|
| 1 | 12 |
| 2 | 14 |
| 3 | 46 |
| 4 | 114 |
| 5 | 103 |
| 6 | 77 |
| 7 | 46 |
ggplot(data = InputData, mapping = aes(x = hsc3_T1, fill = factor(hsc3_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc4_T1の度数分布
hsc4_T1_count <- dplyr::count(InputData, hsc4_T1)
knitr::kable(hsc4_T1_count) #テーブル化
| hsc4_T1 | n |
|---|---|
| 1 | 25 |
| 2 | 27 |
| 3 | 67 |
| 4 | 117 |
| 5 | 109 |
| 6 | 45 |
| 7 | 22 |
ggplot(data = InputData, mapping = aes(x = hsc4_T1, fill = factor(hsc4_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc5_T1の度数分布
hsc5_T1_count <- dplyr::count(InputData, hsc5_T1)
knitr::kable(hsc5_T1_count) #テーブル化
| hsc5_T1 | n |
|---|---|
| 1 | 9 |
| 2 | 14 |
| 3 | 26 |
| 4 | 77 |
| 5 | 90 |
| 6 | 100 |
| 7 | 96 |
ggplot(data = InputData, mapping = aes(x = hsc5_T1, fill = factor(hsc5_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc6_T1の度数分布
hsc6_T1_count <- dplyr::count(InputData, hsc6_T1)
knitr::kable(hsc6_T1_count) #テーブル化
| hsc6_T1 | n |
|---|---|
| 1 | 7 |
| 2 | 15 |
| 3 | 37 |
| 4 | 104 |
| 5 | 120 |
| 6 | 83 |
| 7 | 46 |
ggplot(data = InputData, mapping = aes(x = hsc6_T1, fill = factor(hsc6_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc7_T1の度数分布
hsc7_T1_count <- dplyr::count(InputData, hsc7_T1)
knitr::kable(hsc7_T1_count) #テーブル化
| hsc7_T1 | n |
|---|---|
| 1 | 6 |
| 2 | 18 |
| 3 | 49 |
| 4 | 116 |
| 5 | 81 |
| 6 | 87 |
| 7 | 55 |
ggplot(data = InputData, mapping = aes(x = hsc7_T1, fill = factor(hsc7_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc8_T1の度数分布
hsc8_T1_count <- dplyr::count(InputData, hsc8_T1)
knitr::kable(hsc8_T1_count) #テーブル化
| hsc8_T1 | n |
|---|---|
| 1 | 13 |
| 2 | 13 |
| 3 | 51 |
| 4 | 161 |
| 5 | 94 |
| 6 | 54 |
| 7 | 26 |
ggplot(data = InputData, mapping = aes(x = hsc8_T1, fill = factor(hsc8_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc9_T1の度数分布
hsc9_T1_count <- dplyr::count(InputData, hsc9_T1)
knitr::kable(hsc9_T1_count) #テーブル化
| hsc9_T1 | n |
|---|---|
| 1 | 8 |
| 2 | 28 |
| 3 | 76 |
| 4 | 162 |
| 5 | 78 |
| 6 | 45 |
| 7 | 15 |
ggplot(data = InputData, mapping = aes(x = hsc9_T1, fill = factor(hsc9_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc10_T1の度数分布
hsc10_T1_count <- dplyr::count(InputData, hsc10_T1)
knitr::kable(hsc10_T1_count) #テーブル化
| hsc10_T1 | n |
|---|---|
| 1 | 8 |
| 2 | 16 |
| 3 | 18 |
| 4 | 36 |
| 5 | 71 |
| 6 | 121 |
| 7 | 142 |
ggplot(data = InputData, mapping = aes(x = hsc10_T1, fill = factor(hsc10_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc11_T1の度数分布
hsc11_T1_count <- dplyr::count(InputData, hsc11_T1)
knitr::kable(hsc11_T1_count) #テーブル化
| hsc11_T1 | n |
|---|---|
| 1 | 5 |
| 2 | 19 |
| 3 | 48 |
| 4 | 138 |
| 5 | 91 |
| 6 | 69 |
| 7 | 42 |
ggplot(data = InputData, mapping = aes(x = hsc11_T1, fill = factor(hsc11_T1))) + geom_histogram(binwidth = 1) #視覚化
#hsc12_T1の度数分布
hsc12_T1_count <- dplyr::count(InputData, hsc12_T1)
knitr::kable(hsc12_T1_count) #テーブル化
| hsc12_T1 | n |
|---|---|
| 1 | 11 |
| 2 | 17 |
| 3 | 45 |
| 4 | 130 |
| 5 | 109 |
| 6 | 60 |
| 7 | 40 |
ggplot(data = InputData, mapping = aes(x = hsc12_T1, fill = factor(hsc12_T1))) + geom_histogram(binwidth = 1) #視覚化
#eoe_mean_T1の度数分布
eoe_T1_count <- dplyr::count(InputData, eoe_mean_T1)
knitr::kable(eoe_T1_count) #テーブル化
| eoe_mean_T1 | n |
|---|---|
| 1.0 | 2 |
| 1.2 | 1 |
| 1.8 | 2 |
| 2.0 | 1 |
| 2.2 | 3 |
| 2.4 | 2 |
| 2.6 | 5 |
| 2.8 | 3 |
| 3.0 | 9 |
| 3.2 | 5 |
| 3.4 | 11 |
| 3.6 | 27 |
| 3.8 | 38 |
| 4.0 | 49 |
| 4.2 | 42 |
| 4.4 | 36 |
| 4.6 | 32 |
| 4.8 | 29 |
| 5.0 | 23 |
| 5.2 | 23 |
| 5.4 | 20 |
| 5.6 | 11 |
| 5.8 | 12 |
| 6.0 | 5 |
| 6.2 | 8 |
| 6.4 | 5 |
| 6.6 | 3 |
| 6.8 | 2 |
| 7.0 | 3 |
ggplot(data = InputData, mapping = aes(x = eoe_mean_T1, fill = factor(eoe_mean_T1))) + geom_histogram(binwidth = 0.2) #視覚化
#lst_mean_T1の度数分布
lst_T1_count <- dplyr::count(InputData, lst_mean_T1)
knitr::kable(lst_T1_count) #テーブル化
| lst_mean_T1 | n |
|---|---|
| 1.0 | 3 |
| 1.5 | 4 |
| 2.0 | 8 |
| 2.5 | 22 |
| 3.0 | 30 |
| 3.5 | 45 |
| 4.0 | 85 |
| 4.5 | 67 |
| 5.0 | 53 |
| 5.5 | 38 |
| 6.0 | 29 |
| 6.5 | 15 |
| 7.0 | 13 |
ggplot(data = InputData, mapping = aes(x = lst_mean_T1, fill = factor(lst_mean_T1))) + geom_histogram(binwidth = 0.4) #視覚化
#aes_mean_T1の度数分布
aes_T1_count <- dplyr::count(InputData, aes_mean_T1)
knitr::kable(aes_T1_count) #テーブル化
| aes_mean_T1 | n |
|---|---|
| 1.00 | 1 |
| 1.25 | 1 |
| 1.75 | 1 |
| 2.00 | 2 |
| 2.25 | 2 |
| 2.50 | 2 |
| 2.75 | 4 |
| 3.00 | 6 |
| 3.25 | 10 |
| 3.50 | 13 |
| 3.75 | 18 |
| 4.00 | 28 |
| 4.25 | 20 |
| 4.50 | 39 |
| 4.75 | 30 |
| 5.00 | 49 |
| 5.25 | 39 |
| 5.50 | 45 |
| 5.75 | 23 |
| 6.00 | 25 |
| 6.25 | 26 |
| 6.50 | 18 |
| 6.75 | 5 |
| 7.00 | 5 |
ggplot(data = InputData, mapping = aes(x = aes_mean_T1, fill = factor(aes_mean_T1))) + geom_histogram(binwidth = 0.3) #視覚化
#hsc_mean_T1の度数分布
hsc_T1_count <- dplyr::count(InputData, hsc_mean_T1)
knitr::kable(hsc_T1_count) #テーブル化
| hsc_mean_T1 | n |
|---|---|
| 1.000000 | 1 |
| 2.150000 | 2 |
| 2.216667 | 1 |
| 2.733333 | 1 |
| 2.750000 | 1 |
| 2.833333 | 1 |
| 2.916667 | 1 |
| 2.933333 | 1 |
| 2.966667 | 1 |
| 3.200000 | 1 |
| 3.250000 | 1 |
| 3.266667 | 1 |
| 3.300000 | 2 |
| 3.316667 | 2 |
| 3.333333 | 1 |
| 3.366667 | 1 |
| 3.383333 | 1 |
| 3.400000 | 2 |
| 3.416667 | 2 |
| 3.433333 | 2 |
| 3.450000 | 2 |
| 3.483333 | 2 |
| 3.500000 | 1 |
| 3.516667 | 4 |
| 3.533333 | 2 |
| 3.550000 | 2 |
| 3.600000 | 1 |
| 3.616667 | 2 |
| 3.650000 | 1 |
| 3.666667 | 2 |
| 3.683333 | 2 |
| 3.700000 | 3 |
| 3.733333 | 1 |
| 3.750000 | 4 |
| 3.766667 | 1 |
| 3.783333 | 2 |
| 3.816667 | 3 |
| 3.833333 | 5 |
| 3.850000 | 4 |
| 3.866667 | 4 |
| 3.883333 | 1 |
| 3.900000 | 1 |
| 3.916667 | 1 |
| 3.933333 | 4 |
| 3.950000 | 1 |
| 3.966667 | 1 |
| 3.983333 | 3 |
| 4.000000 | 9 |
| 4.016667 | 4 |
| 4.033333 | 1 |
| 4.050000 | 4 |
| 4.066667 | 4 |
| 4.083333 | 2 |
| 4.100000 | 5 |
| 4.116667 | 3 |
| 4.133333 | 3 |
| 4.150000 | 1 |
| 4.166667 | 5 |
| 4.183333 | 3 |
| 4.200000 | 4 |
| 4.216667 | 3 |
| 4.233333 | 4 |
| 4.250000 | 3 |
| 4.266667 | 4 |
| 4.283333 | 2 |
| 4.300000 | 3 |
| 4.316667 | 2 |
| 4.333333 | 4 |
| 4.350000 | 4 |
| 4.366667 | 5 |
| 4.383333 | 1 |
| 4.400000 | 3 |
| 4.416667 | 5 |
| 4.433333 | 1 |
| 4.450000 | 4 |
| 4.466667 | 5 |
| 4.483333 | 1 |
| 4.500000 | 3 |
| 4.516667 | 3 |
| 4.533333 | 3 |
| 4.550000 | 2 |
| 4.566667 | 2 |
| 4.583333 | 3 |
| 4.600000 | 10 |
| 4.616667 | 3 |
| 4.633333 | 6 |
| 4.650000 | 5 |
| 4.666667 | 5 |
| 4.683333 | 4 |
| 4.700000 | 8 |
| 4.733333 | 5 |
| 4.750000 | 2 |
| 4.766667 | 3 |
| 4.783333 | 2 |
| 4.800000 | 6 |
| 4.816667 | 5 |
| 4.833333 | 5 |
| 4.850000 | 2 |
| 4.866667 | 2 |
| 4.883333 | 2 |
| 4.900000 | 4 |
| 4.916667 | 3 |
| 4.933333 | 3 |
| 4.950000 | 1 |
| 4.966667 | 5 |
| 4.983333 | 2 |
| 5.000000 | 1 |
| 5.016667 | 3 |
| 5.033333 | 3 |
| 5.066667 | 1 |
| 5.083333 | 1 |
| 5.100000 | 1 |
| 5.133333 | 3 |
| 5.150000 | 4 |
| 5.166667 | 3 |
| 5.200000 | 1 |
| 5.216667 | 4 |
| 5.233333 | 4 |
| 5.250000 | 2 |
| 5.266667 | 3 |
| 5.283333 | 4 |
| 5.300000 | 4 |
| 5.316667 | 1 |
| 5.333333 | 2 |
| 5.350000 | 1 |
| 5.383333 | 3 |
| 5.400000 | 1 |
| 5.416667 | 1 |
| 5.433333 | 2 |
| 5.450000 | 3 |
| 5.466667 | 1 |
| 5.483333 | 4 |
| 5.500000 | 4 |
| 5.516667 | 3 |
| 5.533333 | 4 |
| 5.566667 | 2 |
| 5.583333 | 1 |
| 5.600000 | 2 |
| 5.633333 | 1 |
| 5.650000 | 1 |
| 5.666667 | 1 |
| 5.716667 | 1 |
| 5.766667 | 1 |
| 5.783333 | 1 |
| 5.800000 | 2 |
| 5.816667 | 5 |
| 5.833333 | 1 |
| 5.850000 | 2 |
| 5.866667 | 1 |
| 5.883333 | 1 |
| 5.900000 | 1 |
| 5.933333 | 1 |
| 5.950000 | 2 |
| 6.033333 | 2 |
| 6.050000 | 1 |
| 6.083333 | 1 |
| 6.100000 | 1 |
| 6.116667 | 1 |
| 6.150000 | 1 |
| 6.166667 | 2 |
| 6.233333 | 1 |
| 6.300000 | 1 |
| 6.333333 | 1 |
| 6.466667 | 1 |
| 6.550000 | 1 |
| 6.800000 | 1 |
| 7.000000 | 1 |
ggplot(data = InputData, mapping = aes(x = hsc_mean_T1, fill = factor(hsc_mean_T1))) +
geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
#hsc1_T2の度数分布
hsc1_T2_count <- dplyr::count(InputData, hsc1_T2)
knitr::kable(hsc1_T2_count) #テーブル化
| hsc1_T2 | n |
|---|---|
| 1 | 6 |
| 2 | 16 |
| 3 | 43 |
| 4 | 160 |
| 5 | 89 |
| 6 | 24 |
| 7 | 6 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc1_T2, fill = factor(hsc1_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc2_T2の度数分布
hsc2_T2_count <- dplyr::count(InputData, hsc2_T2)
knitr::kable(hsc2_T2_count) #テーブル化
| hsc2_T2 | n |
|---|---|
| 1 | 14 |
| 2 | 21 |
| 3 | 74 |
| 4 | 113 |
| 5 | 84 |
| 6 | 28 |
| 7 | 10 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc2_T2, fill = factor(hsc2_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc3_T2の度数分布
hsc3_T2_count <- dplyr::count(InputData, hsc3_T2)
knitr::kable(hsc3_T2_count) #テーブル化
| hsc3_T2 | n |
|---|---|
| 1 | 3 |
| 2 | 14 |
| 3 | 28 |
| 4 | 109 |
| 5 | 102 |
| 6 | 57 |
| 7 | 31 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc3_T2, fill = factor(hsc3_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc4_T2の度数分布
hsc4_T2_count <- dplyr::count(InputData, hsc4_T2)
knitr::kable(hsc4_T2_count) #テーブル化
| hsc4_T2 | n |
|---|---|
| 1 | 10 |
| 2 | 16 |
| 3 | 50 |
| 4 | 129 |
| 5 | 102 |
| 6 | 24 |
| 7 | 13 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc4_T2, fill = factor(hsc4_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc5_T2の度数分布
hsc5_T2_count <- dplyr::count(InputData, hsc5_T2)
knitr::kable(hsc5_T2_count) #テーブル化
| hsc5_T2 | n |
|---|---|
| 1 | 4 |
| 2 | 10 |
| 3 | 15 |
| 4 | 74 |
| 5 | 88 |
| 6 | 71 |
| 7 | 82 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc5_T2, fill = factor(hsc5_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc6_T2の度数分布
hsc6_T2_count <- dplyr::count(InputData, hsc6_T2)
knitr::kable(hsc6_T2_count) #テーブル化
| hsc6_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 5 |
| 3 | 25 |
| 4 | 113 |
| 5 | 100 |
| 6 | 60 |
| 7 | 36 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc6_T2, fill = factor(hsc6_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc7_T2の度数分布
hsc7_T2_count <- dplyr::count(InputData, hsc7_T2)
knitr::kable(hsc7_T2_count) #テーブル化
| hsc7_T2 | n |
|---|---|
| 1 | 7 |
| 2 | 10 |
| 3 | 25 |
| 4 | 116 |
| 5 | 81 |
| 6 | 57 |
| 7 | 48 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc7_T2, fill = factor(hsc7_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc8_T2の度数分布
hsc8_T2_count <- dplyr::count(InputData, hsc8_T2)
knitr::kable(hsc8_T2_count) #テーブル化
| hsc8_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 11 |
| 3 | 24 |
| 4 | 153 |
| 5 | 90 |
| 6 | 40 |
| 7 | 21 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc8_T2, fill = factor(hsc8_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc9_T2の度数分布
hsc9_T2_count <- dplyr::count(InputData, hsc9_T2)
knitr::kable(hsc9_T2_count) #テーブル化
| hsc9_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 17 |
| 3 | 55 |
| 4 | 163 |
| 5 | 67 |
| 6 | 28 |
| 7 | 9 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc9_T2, fill = factor(hsc9_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc10_T2の度数分布
hsc10_T2_count <- dplyr::count(InputData, hsc10_T2)
knitr::kable(hsc10_T2_count) #テーブル化
| hsc10_T2 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 9 |
| 4 | 62 |
| 5 | 69 |
| 6 | 95 |
| 7 | 102 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc10_T2, fill = factor(hsc10_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc11_T2の度数分布
hsc11_T2_count <- dplyr::count(InputData, hsc11_T2)
knitr::kable(hsc11_T2_count) #テーブル化
| hsc11_T2 | n |
|---|---|
| 1 | 3 |
| 2 | 19 |
| 3 | 35 |
| 4 | 113 |
| 5 | 86 |
| 6 | 57 |
| 7 | 31 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc11_T2, fill = factor(hsc11_T2))) + geom_histogram(binwidth = 1) #視覚化
#hsc12_T2の度数分布
hsc12_T2_count <- dplyr::count(InputData, hsc12_T2)
knitr::kable(hsc12_T2_count) #テーブル化
| hsc12_T2 | n |
|---|---|
| 1 | 6 |
| 2 | 10 |
| 3 | 27 |
| 4 | 135 |
| 5 | 87 |
| 6 | 54 |
| 7 | 25 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc12_T2, fill = factor(hsc12_T2))) + geom_histogram(binwidth = 1) #視覚化
#eoe_mean_T2の度数分布
eoe_T2_count <- dplyr::count(InputData, eoe_mean_T2)
knitr::kable(eoe_T2_count) #テーブル化
| eoe_mean_T2 | n |
|---|---|
| 1.0 | 2 |
| 1.6 | 1 |
| 2.0 | 1 |
| 2.2 | 3 |
| 2.4 | 1 |
| 2.6 | 3 |
| 2.8 | 1 |
| 3.0 | 4 |
| 3.2 | 5 |
| 3.4 | 5 |
| 3.6 | 12 |
| 3.8 | 25 |
| 4.0 | 69 |
| 4.2 | 36 |
| 4.4 | 35 |
| 4.6 | 27 |
| 4.8 | 22 |
| 5.0 | 19 |
| 5.2 | 22 |
| 5.4 | 14 |
| 5.6 | 7 |
| 5.8 | 8 |
| 6.0 | 2 |
| 6.2 | 6 |
| 6.4 | 7 |
| 6.6 | 2 |
| 6.8 | 3 |
| 7.0 | 2 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = eoe_mean_T2, fill = factor(eoe_mean_T2))) + geom_histogram(binwidth = 0.2) #視覚化
#lst_mean_T2の度数分布
lst_T2_count <- dplyr::count(InputData, lst_mean_T2)
knitr::kable(lst_T2_count) #テーブル化
| lst_mean_T2 | n |
|---|---|
| 1.0 | 1 |
| 1.5 | 4 |
| 2.0 | 8 |
| 2.5 | 11 |
| 3.0 | 22 |
| 3.5 | 43 |
| 4.0 | 98 |
| 4.5 | 44 |
| 5.0 | 36 |
| 5.5 | 43 |
| 6.0 | 20 |
| 6.5 | 8 |
| 7.0 | 6 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = lst_mean_T2, fill = factor(lst_mean_T2))) + geom_histogram(binwidth = 0.4) #視覚化
#aes_mean_T2の度数分布
aes_T2_count <- dplyr::count(InputData, aes_mean_T2)
knitr::kable(aes_T2_count) #テーブル化
| aes_mean_T2 | n |
|---|---|
| 2.00 | 1 |
| 2.25 | 1 |
| 2.75 | 1 |
| 3.00 | 5 |
| 3.25 | 4 |
| 3.50 | 9 |
| 3.75 | 11 |
| 4.00 | 36 |
| 4.25 | 32 |
| 4.50 | 29 |
| 4.75 | 39 |
| 5.00 | 37 |
| 5.25 | 29 |
| 5.50 | 26 |
| 5.75 | 28 |
| 6.00 | 28 |
| 6.25 | 13 |
| 6.50 | 7 |
| 6.75 | 3 |
| 7.00 | 5 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = aes_mean_T2, fill = factor(aes_mean_T2))) + geom_histogram(binwidth = 0.3) #視覚化
#hsc_mean_T2の度数分布
hsc_T2_count <- dplyr::count(InputData, hsc_mean_T2)
knitr::kable(hsc_T2_count) #テーブル化
| hsc_mean_T2 | n |
|---|---|
| 1.750000 | 1 |
| 2.433333 | 1 |
| 2.733333 | 1 |
| 2.866667 | 1 |
| 2.950000 | 1 |
| 3.166667 | 1 |
| 3.200000 | 1 |
| 3.216667 | 1 |
| 3.250000 | 1 |
| 3.333333 | 2 |
| 3.400000 | 1 |
| 3.433333 | 2 |
| 3.466667 | 1 |
| 3.500000 | 1 |
| 3.533333 | 1 |
| 3.616667 | 2 |
| 3.633333 | 2 |
| 3.666667 | 2 |
| 3.683333 | 1 |
| 3.733333 | 2 |
| 3.750000 | 3 |
| 3.766667 | 3 |
| 3.783333 | 1 |
| 3.800000 | 1 |
| 3.816667 | 2 |
| 3.833333 | 5 |
| 3.850000 | 3 |
| 3.866667 | 1 |
| 3.883333 | 1 |
| 3.900000 | 1 |
| 3.916667 | 5 |
| 3.933333 | 2 |
| 3.950000 | 3 |
| 3.966667 | 1 |
| 3.983333 | 3 |
| 4.000000 | 20 |
| 4.016667 | 1 |
| 4.033333 | 1 |
| 4.050000 | 3 |
| 4.066667 | 3 |
| 4.083333 | 3 |
| 4.100000 | 4 |
| 4.133333 | 3 |
| 4.150000 | 3 |
| 4.166667 | 5 |
| 4.183333 | 3 |
| 4.200000 | 2 |
| 4.216667 | 4 |
| 4.233333 | 4 |
| 4.250000 | 8 |
| 4.266667 | 1 |
| 4.283333 | 1 |
| 4.300000 | 2 |
| 4.316667 | 4 |
| 4.333333 | 5 |
| 4.350000 | 1 |
| 4.366667 | 4 |
| 4.383333 | 2 |
| 4.400000 | 3 |
| 4.416667 | 4 |
| 4.433333 | 3 |
| 4.450000 | 5 |
| 4.466667 | 4 |
| 4.483333 | 1 |
| 4.500000 | 3 |
| 4.516667 | 4 |
| 4.550000 | 4 |
| 4.566667 | 4 |
| 4.583333 | 2 |
| 4.600000 | 1 |
| 4.616667 | 4 |
| 4.633333 | 2 |
| 4.650000 | 4 |
| 4.666667 | 5 |
| 4.683333 | 5 |
| 4.700000 | 4 |
| 4.716667 | 3 |
| 4.733333 | 3 |
| 4.750000 | 1 |
| 4.766667 | 2 |
| 4.783333 | 6 |
| 4.800000 | 2 |
| 4.816667 | 7 |
| 4.833333 | 2 |
| 4.850000 | 1 |
| 4.866667 | 1 |
| 4.883333 | 5 |
| 4.900000 | 7 |
| 4.916667 | 1 |
| 4.933333 | 3 |
| 4.983333 | 2 |
| 5.000000 | 4 |
| 5.016667 | 2 |
| 5.033333 | 3 |
| 5.050000 | 1 |
| 5.066667 | 1 |
| 5.083333 | 3 |
| 5.100000 | 3 |
| 5.116667 | 2 |
| 5.133333 | 3 |
| 5.166667 | 1 |
| 5.183333 | 2 |
| 5.216667 | 2 |
| 5.233333 | 3 |
| 5.250000 | 2 |
| 5.266667 | 2 |
| 5.300000 | 1 |
| 5.316667 | 2 |
| 5.333333 | 3 |
| 5.350000 | 2 |
| 5.383333 | 5 |
| 5.400000 | 3 |
| 5.416667 | 1 |
| 5.450000 | 1 |
| 5.483333 | 1 |
| 5.516667 | 1 |
| 5.533333 | 2 |
| 5.550000 | 1 |
| 5.566667 | 2 |
| 5.583333 | 1 |
| 5.633333 | 2 |
| 5.683333 | 2 |
| 5.700000 | 1 |
| 5.716667 | 1 |
| 5.733333 | 2 |
| 5.766667 | 2 |
| 5.816667 | 1 |
| 5.866667 | 1 |
| 5.883333 | 1 |
| 5.916667 | 2 |
| 5.983333 | 2 |
| 6.033333 | 1 |
| 6.050000 | 2 |
| 6.133333 | 1 |
| 6.166667 | 1 |
| 6.200000 | 1 |
| 6.216667 | 1 |
| 6.433333 | 1 |
| 6.533333 | 1 |
| 6.600000 | 1 |
| 7.000000 | 1 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = hsc_mean_T2, fill = factor(hsc_mean_T2))) +
geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
#health1_T1の度数分布
health1_T1_count <- dplyr::count(InputData, health1_T1)
knitr::kable(health1_T1_count) #テーブル化
| health1_T1 | n |
|---|---|
| 1 | 12 |
| 2 | 75 |
| 3 | 97 |
| 4 | 135 |
| 5 | 72 |
| 6 | 21 |
ggplot(data = InputData, mapping = aes(x = health1_T1, fill = factor(health1_T1))) + geom_histogram(binwidth = 1) #視覚化
#health2_T1の度数分布
health2_T1_count <- dplyr::count(InputData, health2_T1)
knitr::kable(health2_T1_count) #テーブル化
| health2_T1 | n |
|---|---|
| 1 | 11 |
| 2 | 73 |
| 3 | 108 |
| 4 | 126 |
| 5 | 72 |
| 6 | 22 |
ggplot(data = InputData, mapping = aes(x = health2_T1, fill = factor(health2_T1))) + geom_histogram(binwidth = 1) #視覚化
#health3_T1の度数分布
health3_T1_count <- dplyr::count(InputData, health3_T1)
knitr::kable(health3_T1_count) #テーブル化
| health3_T1 | n |
|---|---|
| 1 | 10 |
| 2 | 83 |
| 3 | 97 |
| 4 | 141 |
| 5 | 63 |
| 6 | 18 |
ggplot(data = InputData, mapping = aes(x = health3_T1, fill = factor(health3_T1))) + geom_histogram(binwidth = 1) #視覚化
#health4_T1の度数分布
health4_T1_count <- dplyr::count(InputData, health4_T1)
knitr::kable(health4_T1_count) #テーブル化
| health4_T1 | n |
|---|---|
| 1 | 16 |
| 2 | 90 |
| 3 | 115 |
| 4 | 98 |
| 5 | 69 |
| 6 | 24 |
ggplot(data = InputData, mapping = aes(x = health4_T1, fill = factor(health4_T1))) + geom_histogram(binwidth = 1) #視覚化
#health5_T1の度数分布
health5_T1_count <- dplyr::count(InputData, health5_T1)
knitr::kable(health5_T1_count) #テーブル化
| health5_T1 | n |
|---|---|
| 1 | 18 |
| 2 | 93 |
| 3 | 123 |
| 4 | 113 |
| 5 | 50 |
| 6 | 15 |
ggplot(data = InputData, mapping = aes(x = health5_T1, fill = factor(health5_T1))) + geom_histogram(binwidth = 1) #視覚化
#health_mean_T1の度数分布
health_mean_T1_count <- dplyr::count(InputData, health_mean_T1)
knitr::kable(health_mean_T1_count) #テーブル化
| health_mean_T1 | n |
|---|---|
| 1.0 | 4 |
| 1.2 | 3 |
| 1.4 | 1 |
| 1.6 | 3 |
| 1.8 | 5 |
| 2.0 | 25 |
| 2.2 | 16 |
| 2.4 | 16 |
| 2.6 | 17 |
| 2.8 | 20 |
| 3.0 | 40 |
| 3.2 | 35 |
| 3.4 | 26 |
| 3.6 | 26 |
| 3.8 | 35 |
| 4.0 | 33 |
| 4.2 | 18 |
| 4.4 | 14 |
| 4.6 | 10 |
| 4.8 | 16 |
| 5.0 | 24 |
| 5.2 | 9 |
| 5.4 | 4 |
| 5.6 | 4 |
| 5.8 | 2 |
| 6.0 | 6 |
ggplot(data = InputData, mapping = aes(x = health_mean_T1, fill = factor(health_mean_T1))) + geom_histogram(binwidth = 0.2) #視覚化
#health1_T2の度数分布
health1_T2_count <- dplyr::count(InputData, health1_T2)
knitr::kable(health1_T2_count) #テーブル化
| health1_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 22 |
| 3 | 58 |
| 4 | 147 |
| 5 | 86 |
| 6 | 26 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = health1_T2, fill = factor(health1_T2))) + geom_histogram(binwidth = 1) #視覚化
#health2_T2の度数分布
health2_T2_count <- dplyr::count(InputData, health2_T2)
knitr::kable(health2_T2_count) #テーブル化
| health2_T2 | n |
|---|---|
| 1 | 7 |
| 2 | 30 |
| 3 | 61 |
| 4 | 159 |
| 5 | 71 |
| 6 | 16 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = health2_T2, fill = factor(health2_T2))) + geom_histogram(binwidth = 1) #視覚化
#health3_T2の度数分布
health3_T2_count <- dplyr::count(InputData, health3_T2)
knitr::kable(health3_T2_count) #テーブル化
| health3_T2 | n |
|---|---|
| 1 | 4 |
| 2 | 33 |
| 3 | 68 |
| 4 | 150 |
| 5 | 67 |
| 6 | 22 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = health3_T2, fill = factor(health3_T2))) + geom_histogram(binwidth = 1) #視覚化
#health4_T2の度数分布
health4_T2_count <- dplyr::count(InputData, health4_T2)
knitr::kable(health4_T2_count) #テーブル化
| health4_T2 | n |
|---|---|
| 1 | 8 |
| 2 | 56 |
| 3 | 84 |
| 4 | 104 |
| 5 | 76 |
| 6 | 16 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = health4_T2, fill = factor(health4_T2))) + geom_histogram(binwidth = 1) #視覚化
#health5_T2の度数分布
health5_T2_count <- dplyr::count(InputData, health5_T2)
knitr::kable(health5_T2_count) #テーブル化
| health5_T2 | n |
|---|---|
| 1 | 7 |
| 2 | 42 |
| 3 | 70 |
| 4 | 126 |
| 5 | 74 |
| 6 | 25 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = health5_T2, fill = factor(health5_T2))) + geom_histogram(binwidth = 1) #視覚化
#health_mean_T2の度数分布
health_mean_T2_count <- dplyr::count(InputData, health_mean_T2)
knitr::kable(health_mean_T2_count) #テーブル化
| health_mean_T2 | n |
|---|---|
| 1.0 | 3 |
| 1.4 | 1 |
| 1.8 | 5 |
| 2.0 | 7 |
| 2.2 | 5 |
| 2.4 | 10 |
| 2.6 | 7 |
| 2.8 | 11 |
| 3.0 | 17 |
| 3.2 | 15 |
| 3.4 | 20 |
| 3.6 | 26 |
| 3.8 | 36 |
| 4.0 | 56 |
| 4.2 | 25 |
| 4.4 | 18 |
| 4.6 | 15 |
| 4.8 | 13 |
| 5.0 | 29 |
| 5.2 | 11 |
| 5.4 | 2 |
| 5.6 | 2 |
| 5.8 | 3 |
| 6.0 | 7 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = health_mean_T2, fill = factor(health_mean_T2))) + geom_histogram(binwidth = 0.2) #視覚化
#panas1_T1の度数分布
panas1_T1_count <- dplyr::count(InputData, panas1_T1)
knitr::kable(panas1_T1_count) #テーブル化
| panas1_T1 | n |
|---|---|
| 1 | 89 |
| 2 | 134 |
| 3 | 117 |
| 4 | 52 |
| 5 | 17 |
| 6 | 3 |
ggplot(data = InputData, mapping = aes(x = panas1_T1, fill = factor(panas1_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas2_T1の度数分布
panas2_T1_count <- dplyr::count(InputData, panas2_T1)
knitr::kable(panas2_T1_count) #テーブル化
| panas2_T1 | n |
|---|---|
| 1 | 8 |
| 2 | 35 |
| 3 | 112 |
| 4 | 165 |
| 5 | 87 |
| 6 | 5 |
ggplot(data = InputData, mapping = aes(x = panas2_T1, fill = factor(panas2_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas3_T1の度数分布
panas3_T1_count <- dplyr::count(InputData, panas3_T1)
knitr::kable(panas3_T1_count) #テーブル化
| panas3_T1 | n |
|---|---|
| 1 | 106 |
| 2 | 136 |
| 3 | 122 |
| 4 | 38 |
| 5 | 8 |
| 6 | 2 |
ggplot(data = InputData, mapping = aes(x = panas3_T1, fill = factor(panas3_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas4_T1の度数分布
panas4_T1_count <- dplyr::count(InputData, panas4_T1)
knitr::kable(panas4_T1_count) #テーブル化
| panas4_T1 | n |
|---|---|
| 1 | 22 |
| 2 | 57 |
| 3 | 172 |
| 4 | 122 |
| 5 | 35 |
| 6 | 4 |
ggplot(data = InputData, mapping = aes(x = panas4_T1, fill = factor(panas4_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas5_T1の度数分布
panas5_T1_count <- dplyr::count(InputData, panas5_T1)
knitr::kable(panas5_T1_count) #テーブル化
| panas5_T1 | n |
|---|---|
| 1 | 68 |
| 2 | 123 |
| 3 | 161 |
| 4 | 44 |
| 5 | 11 |
| 6 | 5 |
ggplot(data = InputData, mapping = aes(x = panas5_T1, fill = factor(panas5_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas6_T1の度数分布
panas6_T1_count <- dplyr::count(InputData, panas6_T1)
knitr::kable(panas6_T1_count) #テーブル化
| panas6_T1 | n |
|---|---|
| 1 | 20 |
| 2 | 70 |
| 3 | 168 |
| 4 | 112 |
| 5 | 37 |
| 6 | 5 |
ggplot(data = InputData, mapping = aes(x = panas6_T1, fill = factor(panas6_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas7_T1の度数分布
panas7_T1_count <- dplyr::count(InputData, panas7_T1)
knitr::kable(panas7_T1_count) #テーブル化
| panas7_T1 | n |
|---|---|
| 1 | 20 |
| 2 | 54 |
| 3 | 138 |
| 4 | 134 |
| 5 | 56 |
| 6 | 10 |
ggplot(data = InputData, mapping = aes(x = panas7_T1, fill = factor(panas7_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas8_T1の度数分布
panas8_T1_count <- dplyr::count(InputData, panas8_T1)
knitr::kable(panas8_T1_count) #テーブル化
| panas8_T1 | n |
|---|---|
| 1 | 10 |
| 2 | 36 |
| 3 | 135 |
| 4 | 157 |
| 5 | 57 |
| 6 | 17 |
ggplot(data = InputData, mapping = aes(x = panas8_T1, fill = factor(panas8_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas9_T1の度数分布
panas9_T1_count <- dplyr::count(InputData, panas9_T1)
knitr::kable(panas9_T1_count) #テーブル化
| panas9_T1 | n |
|---|---|
| 1 | 31 |
| 2 | 88 |
| 3 | 137 |
| 4 | 106 |
| 5 | 37 |
| 6 | 13 |
ggplot(data = InputData, mapping = aes(x = panas9_T1, fill = factor(panas9_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas10_T1の度数分布
panas10_T1_count <- dplyr::count(InputData, panas10_T1)
knitr::kable(panas10_T1_count) #テーブル化
| panas10_T1 | n |
|---|---|
| 1 | 19 |
| 2 | 53 |
| 3 | 175 |
| 4 | 128 |
| 5 | 32 |
| 6 | 5 |
ggplot(data = InputData, mapping = aes(x = panas10_T1, fill = factor(panas10_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas11_T1の度数分布
panas11_T1_count <- dplyr::count(InputData, panas11_T1)
knitr::kable(panas11_T1_count) #テーブル化
| panas11_T1 | n |
|---|---|
| 1 | 36 |
| 2 | 79 |
| 3 | 152 |
| 4 | 95 |
| 5 | 39 |
| 6 | 11 |
ggplot(data = InputData, mapping = aes(x = panas11_T1, fill = factor(panas11_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas12_T1の度数分布
panas12_T1_count <- dplyr::count(InputData, panas12_T1)
knitr::kable(panas12_T1_count) #テーブル化
| panas12_T1 | n |
|---|---|
| 1 | 16 |
| 2 | 54 |
| 3 | 128 |
| 4 | 138 |
| 5 | 62 |
| 6 | 14 |
ggplot(data = InputData, mapping = aes(x = panas12_T1, fill = factor(panas12_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas13_T1の度数分布
panas13_T1_count <- dplyr::count(InputData, panas13_T1)
knitr::kable(panas13_T1_count) #テーブル化
| panas13_T1 | n |
|---|---|
| 1 | 69 |
| 2 | 122 |
| 3 | 163 |
| 4 | 44 |
| 5 | 10 |
| 6 | 4 |
ggplot(data = InputData, mapping = aes(x = panas13_T1, fill = factor(panas13_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas14_T1の度数分布
panas14_T1_count <- dplyr::count(InputData, panas14_T1)
knitr::kable(panas14_T1_count) #テーブル化
| panas14_T1 | n |
|---|---|
| 1 | 28 |
| 2 | 88 |
| 3 | 209 |
| 4 | 67 |
| 5 | 18 |
| 6 | 2 |
ggplot(data = InputData, mapping = aes(x = panas14_T1, fill = factor(panas14_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas15_T1の度数分布
panas15_T1_count <- dplyr::count(InputData, panas15_T1)
knitr::kable(panas15_T1_count) #テーブル化
| panas15_T1 | n |
|---|---|
| 1 | 25 |
| 2 | 68 |
| 3 | 142 |
| 4 | 118 |
| 5 | 49 |
| 6 | 10 |
ggplot(data = InputData, mapping = aes(x = panas15_T1, fill = factor(panas15_T1))) + geom_histogram(binwidth = 1) #視覚化
#panas16_T1の度数分布
panas16_T1_count <- dplyr::count(InputData, panas16_T1)
knitr::kable(panas16_T1_count) #テーブル化
| panas16_T1 | n |
|---|---|
| 1 | 48 |
| 2 | 104 |
| 3 | 167 |
| 4 | 69 |
| 5 | 19 |
| 6 | 5 |
ggplot(data = InputData, mapping = aes(x = panas16_T1, fill = factor(panas16_T1))) + geom_histogram(binwidth = 1) #視覚化
#positive_T1の度数分布
positive_T1_count <- dplyr::count(InputData, positive_mean_T1)
knitr::kable(positive_T1_count) #テーブル化
| positive_mean_T1 | n |
|---|---|
| 1.000 | 2 |
| 1.125 | 1 |
| 1.500 | 2 |
| 1.625 | 3 |
| 1.875 | 4 |
| 2.000 | 7 |
| 2.125 | 4 |
| 2.250 | 12 |
| 2.375 | 7 |
| 2.500 | 11 |
| 2.625 | 16 |
| 2.750 | 17 |
| 2.875 | 26 |
| 3.000 | 36 |
| 3.125 | 40 |
| 3.250 | 32 |
| 3.375 | 21 |
| 3.500 | 26 |
| 3.625 | 26 |
| 3.750 | 26 |
| 3.875 | 20 |
| 4.000 | 15 |
| 4.125 | 16 |
| 4.250 | 14 |
| 4.375 | 10 |
| 4.500 | 6 |
| 4.625 | 3 |
| 4.750 | 3 |
| 4.875 | 2 |
| 5.000 | 1 |
| 5.250 | 2 |
| 5.625 | 1 |
ggplot(data = InputData, mapping = aes(x = positive_mean_T1, fill = factor(positive_mean_T1))) + geom_histogram(binwidth = 0.3) #視覚化
#negative_T1の度数分布
negative_T1_count <- dplyr::count(InputData, negative_mean_T1)
knitr::kable(negative_T1_count) #テーブル化
| negative_mean_T1 | n |
|---|---|
| 1.000 | 9 |
| 1.125 | 2 |
| 1.250 | 3 |
| 1.375 | 3 |
| 1.500 | 5 |
| 1.625 | 5 |
| 1.750 | 6 |
| 1.875 | 11 |
| 2.000 | 22 |
| 2.125 | 14 |
| 2.250 | 22 |
| 2.375 | 15 |
| 2.500 | 17 |
| 2.625 | 30 |
| 2.750 | 18 |
| 2.875 | 27 |
| 3.000 | 35 |
| 3.125 | 38 |
| 3.250 | 24 |
| 3.375 | 18 |
| 3.500 | 19 |
| 3.625 | 20 |
| 3.750 | 10 |
| 3.875 | 1 |
| 4.000 | 9 |
| 4.125 | 5 |
| 4.250 | 8 |
| 4.375 | 5 |
| 4.500 | 2 |
| 4.625 | 2 |
| 4.750 | 2 |
| 4.875 | 2 |
| 5.000 | 1 |
| 5.125 | 1 |
| 5.750 | 1 |
ggplot(data = InputData, mapping = aes(x = negative_mean_T1, fill = factor(negative_mean_T1))) + geom_histogram(binwidth = 0.2) #視覚化
#environment1_T2の度数分布
environment1_T2_count <- dplyr::count(InputData, environment1_T2)
knitr::kable(environment1_T2_count) #テーブル化
| environment1_T2 | n |
|---|---|
| 1 | 2 |
| 2 | 3 |
| 3 | 27 |
| 4 | 105 |
| 5 | 115 |
| 6 | 50 |
| 7 | 42 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment1_T2, fill = factor(environment1_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment2_T2の度数分布
environment2_T2_count <- dplyr::count(InputData, environment2_T2)
knitr::kable(environment2_T2_count) #テーブル化
| environment2_T2 | n |
|---|---|
| 1 | 1 |
| 2 | 5 |
| 3 | 37 |
| 4 | 119 |
| 5 | 99 |
| 6 | 49 |
| 7 | 34 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment2_T2, fill = factor(environment2_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment3_T2の度数分布
environment3_T2_count <- dplyr::count(InputData, environment3_T2)
knitr::kable(environment3_T2_count) #テーブル化
| environment3_T2 | n |
|---|---|
| 1 | 3 |
| 2 | 5 |
| 3 | 36 |
| 4 | 107 |
| 5 | 96 |
| 6 | 66 |
| 7 | 31 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment3_T2, fill = factor(environment3_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment4_T2の度数分布
environment4_T2_count <- dplyr::count(InputData, environment4_T2)
knitr::kable(environment4_T2_count) #テーブル化
| environment4_T2 | n |
|---|---|
| 1 | 2 |
| 2 | 4 |
| 3 | 36 |
| 4 | 143 |
| 5 | 98 |
| 6 | 41 |
| 7 | 20 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment4_T2, fill = factor(environment4_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment5_T2の度数分布
environment5_T2_count <- dplyr::count(InputData, environment5_T2)
knitr::kable(environment5_T2_count) #テーブル化
| environment5_T2 | n |
|---|---|
| 1 | 4 |
| 2 | 8 |
| 3 | 31 |
| 4 | 182 |
| 5 | 71 |
| 6 | 35 |
| 7 | 13 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment5_T2, fill = factor(environment5_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment6_T2の度数分布
environment6_T2_count <- dplyr::count(InputData, environment6_T2)
knitr::kable(environment6_T2_count) #テーブル化
| environment6_T2 | n |
|---|---|
| 1 | 1 |
| 2 | 6 |
| 3 | 35 |
| 4 | 151 |
| 5 | 75 |
| 6 | 48 |
| 7 | 28 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment6_T2, fill = factor(environment6_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment7_T2の度数分布
environment7_T2_count <- dplyr::count(InputData, environment7_T2)
knitr::kable(environment7_T2_count) #テーブル化
| environment7_T2 | n |
|---|---|
| 1 | 12 |
| 2 | 22 |
| 3 | 70 |
| 4 | 118 |
| 5 | 78 |
| 6 | 31 |
| 7 | 13 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment7_T2, fill = factor(environment7_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment8_T2の度数分布
environment8_T2_count <- dplyr::count(InputData, environment8_T2)
knitr::kable(environment8_T2_count) #テーブル化
| environment8_T2 | n |
|---|---|
| 1 | 13 |
| 2 | 34 |
| 3 | 81 |
| 4 | 103 |
| 5 | 63 |
| 6 | 38 |
| 7 | 12 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment8_T2, fill = factor(environment8_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment9_T2の度数分布
environment9_T2_count <- dplyr::count(InputData, environment9_T2)
knitr::kable(environment9_T2_count) #テーブル化
| environment9_T2 | n |
|---|---|
| 1 | 44 |
| 2 | 62 |
| 3 | 105 |
| 4 | 59 |
| 5 | 29 |
| 6 | 23 |
| 7 | 22 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment9_T2, fill = factor(environment9_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment10_T2の度数分布
environment10_T2_count <- dplyr::count(InputData, environment10_T2)
knitr::kable(environment10_T2_count) #テーブル化
| environment10_T2 | n |
|---|---|
| 1 | 28 |
| 2 | 56 |
| 3 | 102 |
| 4 | 67 |
| 5 | 40 |
| 6 | 26 |
| 7 | 25 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment10_T2, fill = factor(environment10_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment11_T2の度数分布
environment11_T2_count <- dplyr::count(InputData, environment11_T2)
knitr::kable(environment11_T2_count) #テーブル化
| environment11_T2 | n |
|---|---|
| 1 | 8 |
| 2 | 17 |
| 3 | 40 |
| 4 | 128 |
| 5 | 83 |
| 6 | 40 |
| 7 | 28 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment11_T2, fill = factor(environment11_T2))) + geom_histogram(binwidth = 1) #視覚化
#environment11_T2の度数分布
environment_mean_T2_count <- dplyr::count(InputData, environment_mean_T2)
knitr::kable(environment_mean_T2_count) #テーブル化
| environment_mean_T2 | n |
|---|---|
| 1.000000 | 1 |
| 1.363636 | 1 |
| 2.272727 | 1 |
| 2.545454 | 1 |
| 2.636364 | 1 |
| 2.818182 | 5 |
| 2.909091 | 1 |
| 3.000000 | 4 |
| 3.090909 | 3 |
| 3.181818 | 6 |
| 3.272727 | 4 |
| 3.363636 | 11 |
| 3.454546 | 7 |
| 3.545454 | 6 |
| 3.636364 | 8 |
| 3.727273 | 13 |
| 3.818182 | 22 |
| 3.909091 | 21 |
| 4.000000 | 25 |
| 4.090909 | 17 |
| 4.181818 | 21 |
| 4.272727 | 21 |
| 4.363636 | 13 |
| 4.454546 | 13 |
| 4.545454 | 13 |
| 4.636364 | 10 |
| 4.727273 | 12 |
| 4.818182 | 9 |
| 4.909091 | 9 |
| 5.000000 | 9 |
| 5.090909 | 1 |
| 5.181818 | 4 |
| 5.272727 | 8 |
| 5.363636 | 8 |
| 5.454546 | 2 |
| 5.545454 | 5 |
| 5.636364 | 6 |
| 5.727273 | 2 |
| 5.818182 | 4 |
| 5.909091 | 3 |
| 6.000000 | 1 |
| 6.090909 | 2 |
| 6.181818 | 3 |
| 6.272727 | 5 |
| 6.545454 | 1 |
| 7.000000 | 1 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment_mean_T2, fill = factor(environment_mean_T2))) +
geom_histogram(binwidth = 0.2) +
guides(fill = "none")#視覚化
#bis1_T2の度数分布
bis1_T2_count <- dplyr::count(InputData, bis1_T2)
knitr::kable(bis1_T2_count) #テーブル化
| bis1_T2 | n |
|---|---|
| 1 | 18 |
| 2 | 137 |
| 3 | 158 |
| 4 | 31 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis1_T2, fill = factor(bis1_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis2_T2の度数分布
bis2_T2_count <- dplyr::count(InputData, bis2_T2)
knitr::kable(bis2_T2_count) #テーブル化
| bis2_T2 | n |
|---|---|
| 1 | 8 |
| 2 | 81 |
| 3 | 175 |
| 4 | 80 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis2_T2, fill = factor(bis2_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis3_T2の度数分布
bis3_T2_count <- dplyr::count(InputData, bis3_T2)
knitr::kable(bis3_T2_count) #テーブル化
| bis3_T2 | n |
|---|---|
| 1 | 13 |
| 2 | 122 |
| 3 | 174 |
| 4 | 35 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis3_T2, fill = factor(bis3_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis4_T2の度数分布
bis4_T2_count <- dplyr::count(InputData, bis4_T2)
knitr::kable(bis4_T2_count) #テーブル化
| bis4_T2 | n |
|---|---|
| 1 | 18 |
| 2 | 157 |
| 3 | 145 |
| 4 | 24 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis4_T2, fill = factor(bis4_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis5_T2の度数分布
bis5_T2_count <- dplyr::count(InputData, bis5_T2)
knitr::kable(bis5_T2_count) #テーブル化
| bis5_T2 | n |
|---|---|
| 1 | 6 |
| 2 | 127 |
| 3 | 181 |
| 4 | 30 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis5_T2, fill = factor(bis5_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis6_T2の度数分布
bis6_T2_count <- dplyr::count(InputData, bis6_T2)
knitr::kable(bis6_T2_count) #テーブル化
| bis6_T2 | n |
|---|---|
| 1 | 20 |
| 2 | 166 |
| 3 | 136 |
| 4 | 22 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis6_T2, fill = factor(bis6_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis7_T2の度数分布
bis7_T2_count <- dplyr::count(InputData, bis7_T2)
knitr::kable(bis7_T2_count) #テーブル化
| bis7_T2 | n |
|---|---|
| 1 | 35 |
| 2 | 170 |
| 3 | 120 |
| 4 | 19 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis7_T2, fill = factor(bis7_T2))) + geom_histogram(binwidth = 1) #視覚化
#bis_mean_T2の度数分布
bis_mean_T2_count <- dplyr::count(InputData, bis_mean_T2)
knitr::kable(bis_mean_T2_count) #テーブル化
| bis_mean_T2 | n |
|---|---|
| 1.000000 | 1 |
| 1.428571 | 1 |
| 1.571429 | 3 |
| 1.714286 | 4 |
| 1.857143 | 9 |
| 2.000000 | 19 |
| 2.142857 | 27 |
| 2.285714 | 36 |
| 2.428571 | 36 |
| 2.571429 | 58 |
| 2.714286 | 50 |
| 2.857143 | 34 |
| 3.000000 | 18 |
| 3.142857 | 17 |
| 3.285714 | 8 |
| 3.428571 | 4 |
| 3.571429 | 10 |
| 3.714286 | 5 |
| 3.857143 | 3 |
| 4.000000 | 1 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bis_mean_T2, fill = factor(bis_mean_T2))) +
geom_histogram(binwidth = 0.1) +
guides(fill = "none") #視覚化
#bas1_T2の度数分布
bas1_T2_count <- dplyr::count(InputData, bas1_T2)
knitr::kable(bas1_T2_count) #テーブル化
| bas1_T2 | n |
|---|---|
| 1 | 7 |
| 2 | 138 |
| 3 | 161 |
| 4 | 38 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas1_T2, fill = factor(bas1_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas2_T2の度数分布
bas2_T2_count <- dplyr::count(InputData, bas2_T2)
knitr::kable(bas2_T2_count) #テーブル化
| bas2_T2 | n |
|---|---|
| 2 | 45 |
| 3 | 193 |
| 4 | 106 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas2_T2, fill = factor(bas2_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas3_T2の度数分布
bas3_T2_count <- dplyr::count(InputData, bas3_T2)
knitr::kable(bas3_T2_count) #テーブル化
| bas3_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 81 |
| 3 | 207 |
| 4 | 51 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas3_T2, fill = factor(bas3_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas4_T2の度数分布
bas4_T2_count <- dplyr::count(InputData, bas4_T2)
knitr::kable(bas4_T2_count) #テーブル化
| bas4_T2 | n |
|---|---|
| 1 | 2 |
| 2 | 60 |
| 3 | 212 |
| 4 | 70 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas4_T2, fill = factor(bas4_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas5_T2の度数分布
bas5_T2_count <- dplyr::count(InputData, bas5_T2)
knitr::kable(bas5_T2_count) #テーブル化
| bas5_T2 | n |
|---|---|
| 1 | 8 |
| 2 | 142 |
| 3 | 161 |
| 4 | 33 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas5_T2, fill = factor(bas5_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas6_T2の度数分布
bas6_T2_count <- dplyr::count(InputData, bas6_T2)
knitr::kable(bas6_T2_count) #テーブル化
| bas6_T2 | n |
|---|---|
| 1 | 17 |
| 2 | 121 |
| 3 | 186 |
| 4 | 20 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas6_T2, fill = factor(bas6_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas7_T2の度数分布
bas7_T2_count <- dplyr::count(InputData, bas7_T2)
knitr::kable(bas7_T2_count) #テーブル化
| bas7_T2 | n |
|---|---|
| 1 | 11 |
| 2 | 128 |
| 3 | 178 |
| 4 | 27 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas7_T2, fill = factor(bas7_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas8_T2の度数分布
bas8_T2_count <- dplyr::count(InputData, bas8_T2)
knitr::kable(bas8_T2_count) #テーブル化
| bas8_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 118 |
| 3 | 190 |
| 4 | 31 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas8_T2, fill = factor(bas8_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas9_T2の度数分布
bas9_T2_count <- dplyr::count(InputData, bas9_T2)
knitr::kable(bas9_T2_count) #テーブル化
| bas9_T2 | n |
|---|---|
| 1 | 24 |
| 2 | 181 |
| 3 | 133 |
| 4 | 6 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas9_T2, fill = factor(bas9_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas10_T2の度数分布
bas10_T2_count <- dplyr::count(InputData, bas10_T2)
knitr::kable(bas10_T2_count) #テーブル化
| bas10_T2 | n |
|---|---|
| 1 | 8 |
| 2 | 100 |
| 3 | 201 |
| 4 | 35 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas10_T2, fill = factor(bas10_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas11_T2の度数分布
bas11_T2_count <- dplyr::count(InputData, bas11_T2)
knitr::kable(bas11_T2_count) #テーブル化
| bas11_T2 | n |
|---|---|
| 1 | 23 |
| 2 | 191 |
| 3 | 118 |
| 4 | 12 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas11_T2, fill = factor(bas11_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas12_T2の度数分布
bas12_T2_count <- dplyr::count(InputData, bas12_T2)
knitr::kable(bas12_T2_count) #テーブル化
| bas12_T2 | n |
|---|---|
| 1 | 14 |
| 2 | 145 |
| 3 | 158 |
| 4 | 27 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas12_T2, fill = factor(bas12_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas13_T2の度数分布
bas13_T2_count <- dplyr::count(InputData, bas13_T2)
knitr::kable(bas13_T2_count) #テーブル化
| bas13_T2 | n |
|---|---|
| 1 | 10 |
| 2 | 104 |
| 3 | 194 |
| 4 | 36 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas13_T2, fill = factor(bas13_T2))) + geom_histogram(binwidth = 1) #視覚化
#bas_mean_T2の度数分布
bas_mean_T2_count <- dplyr::count(InputData, bas_mean_T2)
knitr::kable(bas_mean_T2_count) #テーブル化
| bas_mean_T2 | n |
|---|---|
| 2.857143 | 1 |
| 3.000000 | 1 |
| 3.142857 | 1 |
| 3.428571 | 1 |
| 3.571429 | 3 |
| 3.714286 | 6 |
| 3.857143 | 6 |
| 4.000000 | 14 |
| 4.142857 | 12 |
| 4.285714 | 18 |
| 4.428571 | 17 |
| 4.571429 | 30 |
| 4.714286 | 18 |
| 4.857143 | 18 |
| 5.000000 | 26 |
| 5.142857 | 33 |
| 5.285714 | 25 |
| 5.428571 | 32 |
| 5.571429 | 24 |
| 5.714286 | 18 |
| 5.857143 | 7 |
| 6.000000 | 11 |
| 6.142857 | 8 |
| 6.285714 | 2 |
| 6.428571 | 5 |
| 6.571429 | 3 |
| 6.857143 | 2 |
| 7.000000 | 2 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = bas_mean_T2, fill = factor(bas_mean_T2))) +
geom_histogram(binwidth = 0.2) +
guides(fill = "none") #視覚化
summary(InputData) #一気に出力(SDは出力されない)
## ID gardian_gender_T1 gardian_age_T1 prefecture_T1
## Min. : 8339 Min. :0.000 Min. :32.00 Min. : 1.00
## 1st Qu.: 3547768 1st Qu.:0.000 1st Qu.:43.00 1st Qu.:13.00
## Median : 7551488 Median :1.000 Median :46.00 Median :23.00
## Mean : 8262284 Mean :0.551 Mean :46.24 Mean :22.02
## 3rd Qu.:13230564 3rd Qu.:1.000 3rd Qu.:49.00 3rd Qu.:28.00
## Max. :16625273 Max. :1.000 Max. :59.00 Max. :47.00
##
## area_T1 married_T1 familyincome_T1 pinincome_T1
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. : 1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 1.000
## Median :4.000 Median :2.000 Median : 4.000 Median : 2.000
## Mean :4.403 Mean :1.942 Mean : 3.952 Mean : 2.451
## 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.: 5.000 3rd Qu.: 3.000
## Max. :8.000 Max. :2.000 Max. :10.000 Max. :10.000
## NA's :56 NA's :55
## job_T1 child_gender_T1 hsc1_T1 hsc2_T1
## Min. : 1.000 Min. :0.0 Min. :1.000 Min. :1.000
## 1st Qu.: 4.000 1st Qu.:0.0 1st Qu.:4.000 1st Qu.:3.000
## Median : 6.000 Median :0.5 Median :4.000 Median :4.000
## Mean : 6.221 Mean :0.5 Mean :4.243 Mean :4.148
## 3rd Qu.: 8.000 3rd Qu.:1.0 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :12.000 Max. :1.0 Max. :7.000 Max. :7.000
##
## hsc3_T1 hsc4_T1 hsc5_T1 hsc6_T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :4.000 Median :5.000 Median :5.000
## Mean :4.692 Mean :4.167 Mean :5.206 Mean :4.816
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## hsc7_T1 hsc8_T1 hsc9_T1 hsc10_T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:5.000
## Median :5.000 Median :4.000 Median :4.000 Median :6.000
## Mean :4.769 Mean :4.398 Mean :4.138 Mean :5.614
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## hsc11_T1 hsc12_T1 health1_T1 health2_T1
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.00 1st Qu.:3.000
## Median :4.000 Median :5.000 Median :4.00 Median :4.000
## Mean :4.617 Mean :4.575 Mean :3.59 Mean :3.585
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :7.000 Max. :7.000 Max. :6.00 Max. :6.000
##
## health3_T1 health4_T1 health5_T1 panas1_T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :3.000 Median :3.000 Median :2.000
## Mean :3.529 Mean :3.451 Mean :3.313 Mean :2.473
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## panas2_T1 panas3_T1 panas4_T1 panas5_T1
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:3.00 1st Qu.:2.000
## Median :4.000 Median :2.000 Median :3.00 Median :3.000
## Mean :3.735 Mean :2.301 Mean :3.25 Mean :2.568
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
##
## panas6_T1 panas7_T1 panas8_T1 panas9_T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.000 Median :3.000
## Mean :3.221 Mean :3.442 Mean :3.646 Mean :3.167
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## panas10_T1 panas11_T1 panas12_T1 panas13_T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.000 Median :3.000
## Mean :3.282 Mean :3.133 Mean :3.529 Mean :2.553
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## panas14_T1 panas15_T1 panas16_T1 gardian_gender_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:0.0000
## Median :3.000 Median :3.000 Median :3.000 Median :1.0000
## Mean :2.915 Mean :3.311 Mean :2.811 Mean :0.5291
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :1.0000
## NA's :68
## gardian_age_T2 prefecture_T2 area_T2 married_T2
## Min. :32.0 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.:43.0 1st Qu.:13.00 1st Qu.:3.000 1st Qu.:2.000
## Median :46.0 Median :23.00 Median :4.000 Median :2.000
## Mean :46.4 Mean :22.02 Mean :4.395 Mean :1.942
## 3rd Qu.:50.0 3rd Qu.:28.00 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :59.0 Max. :47.00 Max. :8.000 Max. :2.000
## NA's :68 NA's :68 NA's :68 NA's :68
## familyincome_T2 pinincome_T2 job_T2 child_gender_T2
## Min. : 1.000 Min. : 1.00 Min. : 1.000 Min. :0.0000
## 1st Qu.: 3.000 1st Qu.: 1.00 1st Qu.: 4.000 1st Qu.:0.0000
## Median : 4.000 Median : 2.00 Median : 6.000 Median :0.0000
## Mean : 4.057 Mean : 2.58 Mean : 6.113 Mean :0.4942
## 3rd Qu.: 5.000 3rd Qu.: 3.00 3rd Qu.: 8.000 3rd Qu.:1.0000
## Max. :10.000 Max. :10.00 Max. :12.000 Max. :1.0000
## NA's :112 NA's :112 NA's :68 NA's :68
## environment1_T2 environment2_T2 environment3_T2 environment4_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :4.000
## Mean :4.878 Mean :4.724 Mean :4.773 Mean :4.552
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## NA's :68 NA's :68 NA's :68 NA's :68
## environment5_T2 environment6_T2 environment7_T2 environment8_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.352 Mean :4.596 Mean :4.084 Mean :3.962
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## NA's :68 NA's :68 NA's :68 NA's :68
## environment9_T2 environment10_T2 environment11_T2 hsc1_T2
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.00
## Median :3.00 Median :3.000 Median :4.000 Median :4.00
## Mean :3.36 Mean :3.619 Mean :4.433 Mean :4.18
## 3rd Qu.:4.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.00
## NA's :68 NA's :68 NA's :68 NA's :68
## hsc2_T2 hsc3_T2 hsc4_T2 hsc5_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :5.000 Median :4.000 Median :5.000
## Mean :4.035 Mean :4.709 Mean :4.224 Mean :5.247
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## NA's :68 NA's :68 NA's :68 NA's :68
## hsc6_T2 hsc7_T2 hsc8_T2 hsc9_T2
## Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.0 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :4.0 Median :4.000
## Mean :4.808 Mean :4.794 Mean :4.5 Mean :4.134
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:5.0 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.0 Max. :7.000
## NA's :68 NA's :68 NA's :68 NA's :68
## hsc10_T2 hsc11_T2 hsc12_T2 health1_T2
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.00 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :6.00 Median :5.000 Median :4.000 Median :4.000
## Mean :5.57 Mean :4.613 Mean :4.596 Mean :4.061
## 3rd Qu.:7.00 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :6.000
## NA's :68 NA's :68 NA's :68 NA's :68
## health2_T2 health3_T2 health4_T2 health5_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.887 Mean :3.898 Mean :3.674 Mean :3.852
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :68 NA's :68 NA's :68 NA's :68
## bis1r_T2 bas1_T2 bas2_T2 bas3_T2
## Min. :1.000 Min. :1.000 Min. :2.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.750
## Median :2.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.413 Mean :2.669 Mean :3.177 Mean :2.884
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :68 NA's :68 NA's :68 NA's :68
## bas4_T2 bis2_T2 bas5_T2 bas6_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.017 Mean :2.951 Mean :2.637 Mean :2.608
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :68 NA's :68 NA's :68 NA's :68
## bas7_T2 bis3_T2 bas8_T2 bas9_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :2.000
## Mean :2.642 Mean :2.672 Mean :2.718 Mean :2.352
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :68 NA's :68 NA's :68 NA's :68
## bis4_T2 bas10_T2 bis5_T2 bas11_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000 Median :2.000
## Mean :2.509 Mean :2.765 Mean :2.683 Mean :2.346
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :68 NA's :68 NA's :68 NA's :68
## bas12_T2 bis6r_T2 bas13_T2 bis7_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :2.000
## Mean :2.576 Mean :2.535 Mean :2.744 Mean :2.358
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## NA's :68 NA's :68 NA's :68 NA's :68
## bis1_T2 bis6_T2 eoe_mean_T1 na.rm
## Min. :1.000 Min. :1.000 Min. :1.000 Mode:logical
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.800 TRUE:412
## Median :3.000 Median :2.000 Median :4.400
## Mean :2.587 Mean :2.465 Mean :4.419
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :4.000 Max. :4.000 Max. :7.000
## NA's :68 NA's :68
## eoe_mean_T2 lst_mean_T1 lst_mean_T2 aes_mean_T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.500 1st Qu.:3.500 1st Qu.:4.250
## Median :4.400 Median :4.500 Median :4.000 Median :5.000
## Mean :4.452 Mean :4.382 Mean :4.324 Mean :4.939
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.500
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## NA's :68 NA's :68
## aes_mean_T2 hsc_mean_T1 hsc_mean_T2 health_mean_T1
## Min. :2.000 Min. :1.000 Min. :1.750 Min. :1.000
## 1st Qu.:4.250 1st Qu.:4.050 1st Qu.:4.062 1st Qu.:2.800
## Median :5.000 Median :4.600 Median :4.517 Median :3.400
## Mean :4.927 Mean :4.580 Mean :4.568 Mean :3.505
## 3rd Qu.:5.500 3rd Qu.:5.133 3rd Qu.:5.000 3rd Qu.:4.200
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :6.000
## NA's :68 NA's :68
## health_mean_T2 positive_mean_T1 negative_mean_T1 environment_mean_T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.400 1st Qu.:2.875 1st Qu.:2.375 1st Qu.:3.818
## Median :4.000 Median :3.250 Median :2.875 Median :4.182
## Mean :3.872 Mean :3.299 Mean :2.869 Mean :4.303
## 3rd Qu.:4.400 3rd Qu.:3.750 3rd Qu.:3.375 3rd Qu.:4.727
## Max. :6.000 Max. :5.625 Max. :5.750 Max. :7.000
## NA's :68 NA's :68
## bis_mean_T2 bas_mean_T2
## Min. :1.000 Min. :2.857
## 1st Qu.:2.286 1st Qu.:4.571
## Median :2.571 Median :5.071
## Mean :2.603 Mean :5.019
## 3rd Qu.:2.857 3rd Qu.:5.429
## Max. :4.000 Max. :7.000
## NA's :68 NA's :68
hsc_T1_discriptive <-
InputData %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T1.mean = mean (hsc1_T1), #hsc1_T1の平均
hsc1.T1.sd = sd (hsc1_T1), #hsc1_T1のSD
hsc2.T1.mean = mean (hsc2_T1),
hsc2.T1.sd = sd (hsc2_T1),
hsc3.T1.mean = mean (hsc3_T1),
hsc3.T1.sd = sd (hsc3_T1),
hsc4.T1.mean = mean (hsc4_T1),
hsc4.T1.sd = sd (hsc4_T1),
hsc5.T1.mean = mean (hsc5_T1),
hsc5.T1.sd = sd (hsc5_T1),
hsc6.T1.mean = mean (hsc6_T1),
hsc6.T1.sd = sd (hsc6_T1),
hsc7.T1.mean = mean (hsc7_T1),
hsc7.T1.sd = sd (hsc7_T1),
hsc8.T1.mean = mean (hsc8_T1),
hsc8.T1.sd = sd (hsc8_T1),
hsc9.T1.mean = mean (hsc9_T1),
hsc9.T1.sd = sd (hsc9_T1),
hsc10.T1.mean = mean (hsc10_T1),
hsc10.T1.sd = sd (hsc10_T1),
hsc11.T1.mean = mean (hsc11_T1),
hsc11.T1.sd = sd (hsc11_T1),
hsc12.T1.mean = mean (hsc4_T1),
hsc12.T1.sd = sd (hsc4_T1),
eoe.mean.T1 = mean (eoe_mean_T1),
eoe.sd.T1 = sd (eoe_mean_T1),
lst.mean.T1 = mean (lst_mean_T1),
lst.sd.T1 = sd (lst_mean_T1),
aes.mean.T1 = mean (aes_mean_T1),
aes.sd.T1 = sd (aes_mean_T1),
hsc.mean.T1 = mean (hsc_mean_T1),
hsc.sd.T1 = sd (hsc_mean_T1))
#ditydataにしたほうがbetter
knitr::kable(hsc_T1_discriptive, digits = 2) #出力
| n | hsc1.T1.mean | hsc1.T1.sd | hsc2.T1.mean | hsc2.T1.sd | hsc3.T1.mean | hsc3.T1.sd | hsc4.T1.mean | hsc4.T1.sd | hsc5.T1.mean | hsc5.T1.sd | hsc6.T1.mean | hsc6.T1.sd | hsc7.T1.mean | hsc7.T1.sd | hsc8.T1.mean | hsc8.T1.sd | hsc9.T1.mean | hsc9.T1.sd | hsc10.T1.mean | hsc10.T1.sd | hsc11.T1.mean | hsc11.T1.sd | hsc12.T1.mean | hsc12.T1.sd | eoe.mean.T1 | eoe.sd.T1 | lst.mean.T1 | lst.sd.T1 | aes.mean.T1 | aes.sd.T1 | hsc.mean.T1 | hsc.sd.T1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 412 | 4.24 | 1.25 | 4.15 | 1.49 | 4.69 | 1.43 | 4.17 | 1.46 | 5.21 | 1.49 | 4.82 | 1.35 | 4.77 | 1.44 | 4.4 | 1.3 | 4.14 | 1.25 | 5.61 | 1.49 | 4.62 | 1.36 | 4.17 | 1.46 | 4.42 | 0.95 | 4.38 | 1.2 | 4.94 | 1.03 | 4.58 | 0.8 |
hsc_T2_discriptive <-
InputData %>%
drop_na() %>% #T2は欠損値があるのでdrop_na()を挟む
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T2.mean = mean (hsc1_T2), #hsc1_T2の平均
hsc1.T2.sd = sd (hsc1_T2), #hsc1_T2のSD
hsc2.T2.mean = mean (hsc2_T2),
hsc2.T2.sd = sd (hsc2_T2),
hsc3.T2.mean = mean (hsc3_T2),
hsc3.T2.sd = sd (hsc3_T2),
hsc4.T2.mean = mean (hsc4_T2),
hsc4.T2.sd = sd (hsc4_T2),
hsc5.T2.mean = mean (hsc5_T2),
hsc5.T2.sd = sd (hsc5_T2),
hsc6.T2.mean = mean (hsc6_T2),
hsc6.T2.sd = sd (hsc6_T2),
hsc7.T2.mean = mean (hsc7_T2),
hsc7.T2.sd = sd (hsc7_T2),
hsc8.T2.mean = mean (hsc8_T2),
hsc8.T2.sd = sd (hsc8_T2),
hsc9.T2.mean = mean (hsc9_T2),
hsc9.T2.sd = sd (hsc9_T2),
hsc10.T2.mean = mean (hsc10_T2),
hsc10.T2.sd = sd (hsc10_T2),
hsc11.T2.mean = mean (hsc11_T2),
hsc11.T2.sd = sd (hsc11_T2),
hsc12.T2.mean = mean (hsc4_T2),
hsc12.T2.sd = sd (hsc4_T2),
eoe.mean.T2 = mean (eoe_mean_T2),
eoe.sd.T2 = sd (eoe_mean_T2),
lst.mean.T2 = mean (lst_mean_T2),
lst.sd.T2 = sd (lst_mean_T2),
aes.mean.T2 = mean (aes_mean_T2),
aes.sd.T2 = sd (aes_mean_T2),
hsc.mean.T2 = mean (hsc_mean_T2),
hsc.sd.T2 = sd (hsc_mean_T2))
#ditydataにしたほうがbetter
knitr::kable(hsc_T2_discriptive, digits = 2) #出力
| n | hsc1.T2.mean | hsc1.T2.sd | hsc2.T2.mean | hsc2.T2.sd | hsc3.T2.mean | hsc3.T2.sd | hsc4.T2.mean | hsc4.T2.sd | hsc5.T2.mean | hsc5.T2.sd | hsc6.T2.mean | hsc6.T2.sd | hsc7.T2.mean | hsc7.T2.sd | hsc8.T2.mean | hsc8.T2.sd | hsc9.T2.mean | hsc9.T2.sd | hsc10.T2.mean | hsc10.T2.sd | hsc11.T2.mean | hsc11.T2.sd | hsc12.T2.mean | hsc12.T2.sd | eoe.mean.T2 | eoe.sd.T2 | lst.mean.T2 | lst.sd.T2 | aes.mean.T2 | aes.sd.T2 | hsc.mean.T2 | hsc.sd.T2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 295 | 4.22 | 1.05 | 4.05 | 1.29 | 4.7 | 1.28 | 4.22 | 1.22 | 5.27 | 1.38 | 4.8 | 1.25 | 4.8 | 1.39 | 4.5 | 1.18 | 4.17 | 1.15 | 5.6 | 1.28 | 4.61 | 1.35 | 4.22 | 1.22 | 4.46 | 0.92 | 4.33 | 1.13 | 4.95 | 0.89 | 4.58 | 0.72 |
health_T1_discriptive <-
InputData %>%
dplyr::summarise(n = n (), #グループの人数を出力
health1.T1.mean = mean (health1_T1), #health1_T1の平均
health1.T1.sd = sd (health1_T1), #health1_T1のSD
health2.T1.mean = mean (health2_T1),
health2.T1.sd = sd (health2_T1),
health3.T1.mean = mean (health3_T1),
health3.T1.sd = sd (health3_T1),
health4.T1.mean = mean (health4_T1),
health4.T1.sd = sd (health4_T1),
health5.T1.mean = mean (health5_T1),
health5.T1.sd = sd (health5_T1),
health.mean.T1 = mean (health_mean_T1),
health.sd.T1 = sd (health_mean_T1))
knitr::kable(health_T1_discriptive, digits = 2) #出力
| n | health1.T1.mean | health1.T1.sd | health2.T1.mean | health2.T1.sd | health3.T1.mean | health3.T1.sd | health4.T1.mean | health4.T1.sd | health5.T1.mean | health5.T1.sd | health.mean.T1 | health.sd.T1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 412 | 3.59 | 1.2 | 3.58 | 1.2 | 3.53 | 1.17 | 3.45 | 1.27 | 3.31 | 1.18 | 3.5 | 1.04 |
health_T2_discriptive <-
InputData %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
health1.T2.mean = mean (health1_T2), #health1_T2の平均
health1.T2.sd = sd (health1_T2), #health1_T2のSD
health2.T2.mean = mean (health2_T2),
health2.T2.sd = sd (health2_T2),
health3.T2.mean = mean (health3_T2),
health3.T2.sd = sd (health3_T2),
health4.T2.mean = mean (health4_T2),
health4.T2.sd = sd (health4_T2),
health5.T2.mean = mean (health5_T2),
health5.T2.sd = sd (health5_T2),
health.mean.T2 = mean (health_mean_T2),
health.sd.T2 = sd (health_mean_T2))
knitr::kable(health_T2_discriptive, digits = 2) #出力
| n | health1.T2.mean | health1.T2.sd | health2.T2.mean | health2.T2.sd | health3.T2.mean | health3.T2.sd | health4.T2.mean | health4.T2.sd | health5.T2.mean | health5.T2.sd | health.mean.T2 | health.sd.T2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 295 | 4.08 | 1.05 | 3.91 | 1.03 | 3.89 | 1.08 | 3.67 | 1.22 | 3.87 | 1.16 | 3.89 | 0.93 |
panas_T1_discriptive <-
InputData %>%
dplyr::summarise(n = n (), #グループの人数を出力
panas1.T1.mean = mean (panas1_T1), #panas1_T1の平均
panas1.T1.sd = sd (panas1_T1), #panas1_T1のSD
panas2.T1.mean = mean (panas2_T1),
panas2.T1.sd = sd (panas2_T1),
panas3.T1.mean = mean (panas3_T1),
panas3.T1.sd = sd (panas3_T1),
panas4.T1.mean = mean (panas4_T1),
panas4.T1.sd = sd (panas4_T1),
panas5.T1.mean = mean (panas5_T1),
panas5.T1.sd = sd (panas5_T1),
panas6.T1.mean = mean (panas6_T1),
panas6.T1.sd = sd (panas6_T1),
panas7.T1.mean = mean (panas7_T1),
panas7.T1.sd = sd (panas7_T1),
panas8.T1.mean = mean (panas8_T1),
panas8.T1.sd = sd (panas8_T1),
panas9.T1.mean = mean (panas9_T1),
panas9.T1.sd = sd (panas9_T1),
panas10.T1.mean = mean (panas10_T1),
panas10.T1.sd = sd (panas10_T1),
panas11.T1.mean = mean (panas11_T1),
panas11.T1.sd = sd (panas11_T1),
panas12.T1.mean = mean (panas12_T1),
panas12.T1.sd = sd (panas12_T1),
panas13.T1.mean = mean (panas13_T1),
panas13.T1.sd = sd (panas13_T1),
panas14.T1.mean = mean (panas14_T1),
panas14.T1.sd = sd (panas14_T1),
panas15.T1.mean = mean (panas15_T1),
panas15.T1.sd = sd (panas15_T1),
panas16.T1.mean = mean (panas16_T1),
panas16.T1.sd = sd (panas16_T1),
positive.mean.T1 = mean (positive_mean_T1),
positive.sd.T1 = sd (positive_mean_T1),
negative.mean.T1 = mean (negative_mean_T1),
negative.sd.T1 = sd (negative_mean_T1))
knitr::kable(panas_T1_discriptive, digits = 2) #出力
| n | panas1.T1.mean | panas1.T1.sd | panas2.T1.mean | panas2.T1.sd | panas3.T1.mean | panas3.T1.sd | panas4.T1.mean | panas4.T1.sd | panas5.T1.mean | panas5.T1.sd | panas6.T1.mean | panas6.T1.sd | panas7.T1.mean | panas7.T1.sd | panas8.T1.mean | panas8.T1.sd | panas9.T1.mean | panas9.T1.sd | panas10.T1.mean | panas10.T1.sd | panas11.T1.mean | panas11.T1.sd | panas12.T1.mean | panas12.T1.sd | panas13.T1.mean | panas13.T1.sd | panas14.T1.mean | panas14.T1.sd | panas15.T1.mean | panas15.T1.sd | panas16.T1.mean | panas16.T1.sd | positive.mean.T1 | positive.sd.T1 | negative.mean.T1 | negative.sd.T1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 412 | 2.47 | 1.13 | 3.74 | 0.99 | 2.3 | 1.04 | 3.25 | 1.01 | 2.57 | 1.05 | 3.22 | 1.03 | 3.44 | 1.1 | 3.65 | 1.04 | 3.17 | 1.18 | 3.28 | 0.98 | 3.13 | 1.17 | 3.53 | 1.12 | 2.55 | 1.03 | 2.92 | 0.93 | 3.31 | 1.14 | 2.81 | 1.07 | 3.3 | 0.7 | 2.87 | 0.79 |
environment_T2_discriptive <-
InputData %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
environment1.T2.mean = mean (environment1_T2), #environment1_T2の平均
environment1.T2.sd = sd (environment1_T2), #environment1_T2のSD
environment2.T2.mean = mean (environment2_T2),
environment2.T2.sd = sd (environment2_T2),
environment3.T2.mean = mean (environment3_T2),
environment3.T2.sd = sd (environment3_T2),
environment4.T2.mean = mean (environment4_T2),
environment4.T2.sd = sd (environment4_T2),
environment5.T2.mean = mean (environment5_T2),
environment5.T2.sd = sd (environment5_T2),
environment6.T2.mean = mean (environment6_T2),
environment6.T2.sd = sd (environment6_T2),
environment7.T2.mean = mean (environment7_T2),
environment7.T2.sd = sd (environment7_T2),
environment8.T2.mean = mean (environment8_T2),
environment8.T2.sd = sd (environment8_T2),
environment9.T2.mean = mean (environment9_T2),
environment9.T2.sd = sd (environment9_T2),
environment10.T2.mean = mean (environment10_T2),
environment10.T2.sd = sd (environment10_T2),
environment11.T2.mean = mean (environment11_T2),
environment11.T2.sd = sd (environment11_T2),
environment.T2.mean = mean (environment_mean_T2),
environment.T2.sd = sd (environment_mean_T2))
knitr::kable(environment_T2_discriptive, digits = 2) #出力
| n | environment1.T2.mean | environment1.T2.sd | environment2.T2.mean | environment2.T2.sd | environment3.T2.mean | environment3.T2.sd | environment4.T2.mean | environment4.T2.sd | environment5.T2.mean | environment5.T2.sd | environment6.T2.mean | environment6.T2.sd | environment7.T2.mean | environment7.T2.sd | environment8.T2.mean | environment8.T2.sd | environment9.T2.mean | environment9.T2.sd | environment10.T2.mean | environment10.T2.sd | environment11.T2.mean | environment11.T2.sd | environment.T2.mean | environment.T2.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 295 | 4.88 | 1.21 | 4.74 | 1.2 | 4.76 | 1.23 | 4.54 | 1.08 | 4.33 | 1.06 | 4.61 | 1.16 | 4.05 | 1.29 | 3.93 | 1.37 | 3.35 | 1.64 | 3.6 | 1.61 | 4.42 | 1.36 | 4.29 | 0.83 |
bis_T2_discriptive <-
InputData %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
bis1.T2.mean = mean (bis1_T2), #bis1_T2の平均
bis1.T2.sd = sd (bis1_T2), #bis1_T2のSD
bis2.T2.mean = mean (bis2_T2),
bis2.T2.sd = sd (bis2_T2),
bis3.T2.mean = mean (bis3_T2),
bis3.T2.sd = sd (bis3_T2),
bis4.T2.mean = mean (bis4_T2),
bis4.T2.sd = sd (bis4_T2),
bis5.T2.mean = mean (bis5_T2),
bis5.T2.sd = sd (bis5_T2),
bis6.T2.mean = mean (bis6_T2),
bis6.T2.sd = sd (bis6_T2),
bis7.T2.mean = mean (bis7_T2),
bis7.T2.sd = sd (bis7_T2),
bis.T2.mean = mean (bis_mean_T2),
bis.T2.sd = sd (bis_mean_T2))
knitr::kable(bis_T2_discriptive, digits = 2) #出力
| n | bis1.T2.mean | bis1.T2.sd | bis2.T2.mean | bis2.T2.sd | bis3.T2.mean | bis3.T2.sd | bis4.T2.mean | bis4.T2.sd | bis5.T2.mean | bis5.T2.sd | bis6.T2.mean | bis6.T2.sd | bis7.T2.mean | bis7.T2.sd | bis.T2.mean | bis.T2.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 295 | 2.57 | 0.73 | 2.98 | 0.73 | 2.68 | 0.7 | 2.5 | 0.69 | 2.69 | 0.64 | 2.44 | 0.7 | 2.35 | 0.74 | 2.6 | 0.45 |
bas_T2_discriptive <-
InputData %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
bas1.T2.mean = mean (bas1_T2), #bas1_T2の平均
bas1.T2.sd = sd (bas1_T2), #bas1_T2のSD
bas2.T2.mean = mean (bas2_T2),
bas2.T2.sd = sd (bas2_T2),
bas3.T2.mean = mean (bas3_T2),
bas3.T2.sd = sd (bas3_T2),
bas4.T2.mean = mean (bas4_T2),
bas4.T2.sd = sd (bas4_T2),
bas5.T2.mean = mean (bas5_T2),
bas5.T2.sd = sd (bas5_T2),
bas6.T2.mean = mean (bas6_T2),
bas6.T2.sd = sd (bas6_T2),
bas7.T2.mean = mean (bas7_T2),
bas7.T2.sd = sd (bas7_T2),
bas8.T2.mean = mean (bas8_T2),
bas8.T2.sd = sd (bas8_T2),
bas9.T2.mean = mean (bas9_T2),
bas9.T2.sd = sd (bas9_T2),
bas10.T2.mean = mean (bas10_T2),
bas10.T2.sd = sd (bas10_T2),
bas11.T2.mean = mean (bas11_T2),
bas11.T2.sd = sd (bas11_T2),
bas12.T2.mean = mean (bas12_T2),
bas12.T2.sd = sd (bas12_T2),
bas13.T2.mean = mean (bas13_T2),
bas13.T2.sd = sd (bas13_T2),
bas.T2.mean = mean (bas_mean_T2),
bas.T2.sd = sd (bas_mean_T2))
knitr::kable(bas_T2_discriptive, digits = 2) #出力
| n | bas1.T2.mean | bas1.T2.sd | bas2.T2.mean | bas2.T2.sd | bas3.T2.mean | bas3.T2.sd | bas4.T2.mean | bas4.T2.sd | bas5.T2.mean | bas5.T2.sd | bas6.T2.mean | bas6.T2.sd | bas7.T2.mean | bas7.T2.sd | bas8.T2.mean | bas8.T2.sd | bas9.T2.mean | bas9.T2.sd | bas10.T2.mean | bas10.T2.sd | bas11.T2.mean | bas11.T2.sd | bas12.T2.mean | bas12.T2.sd | bas13.T2.mean | bas13.T2.sd | bas.T2.mean | bas.T2.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 295 | 2.66 | 0.7 | 3.18 | 0.64 | 2.87 | 0.65 | 3.01 | 0.63 | 2.65 | 0.68 | 2.62 | 0.68 | 2.66 | 0.67 | 2.72 | 0.64 | 2.33 | 0.62 | 2.77 | 0.64 | 2.34 | 0.67 | 2.6 | 0.69 | 2.72 | 0.66 | 5.02 | 0.7 |
Time1_discriptive_by_gender <-
InputData %>%
dplyr::group_by(child_gender_T1) %>% #性別でグルーピング
dplyr::summarise(n = n (), #グループの人数を出力
hsc_T1_mean = mean (hsc_mean_T1), #hsc_T1の平均
hsc_T1_sd = sd (hsc_mean_T1), #hcs_T1のSD
eoe_T1_mean = mean (eoe_mean_T1), #eoe_T1の平均
eoe_T1_sd = sd (eoe_mean_T1), #eoe_T1のSD
lst_T1_mean = mean (lst_mean_T1), #lst_T1の平均
lst_T1_sd = sd (lst_mean_T1), #lst_T1のSD
aes_T1_mean = mean (aes_mean_T1), #aes_T1の平均
aes_T1_sd = sd (aes_mean_T1), #aes_T1のSD
health_T1_mean = mean (health_mean_T1), #health_T1の平均
health_T1_sd = sd (health_mean_T1), #health_T1のSD
positive_T1_mean = mean (positive_mean_T1), #positive_T1の平均値
positive_T1_sd = sd (positive_mean_T1), #positive_T1のSD
negative_T1_mean = mean (negative_mean_T1), #negative_T1の平均値
negative_T1_sd = sd (negative_mean_T1)) #negative_T1のSD
knitr::kable(Time1_discriptive_by_gender, digits = 2) #出力
| child_gender_T1 | n | hsc_T1_mean | hsc_T1_sd | eoe_T1_mean | eoe_T1_sd | lst_T1_mean | lst_T1_sd | aes_T1_mean | aes_T1_sd | health_T1_mean | health_T1_sd | positive_T1_mean | positive_T1_sd | negative_T1_mean | negative_T1_sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 206 | 4.47 | 0.72 | 4.34 | 0.88 | 4.32 | 1.17 | 4.76 | 0.95 | 3.51 | 1.07 | 3.21 | 0.71 | 2.76 | 0.80 |
| 1 | 206 | 4.69 | 0.86 | 4.50 | 1.00 | 4.45 | 1.24 | 5.12 | 1.07 | 3.50 | 1.01 | 3.38 | 0.68 | 2.97 | 0.77 |
Time2_discriptive_by_gender <-
InputData %>%
drop_na() %>%
dplyr::group_by(child_gender_T1) %>% #性別でグルーピング
dplyr::summarise(n = n (), #グループの人数を出力
hsc_T2_mean = mean (hsc_mean_T2), #hsc_T2の平均
hsc_T2_sd = sd (hsc_mean_T2), #hcs_T2のSD
eoe_T2_mean = mean (eoe_mean_T2), #eoe_T2の平均
eoe_T2_sd = sd (eoe_mean_T2), #eoe_T2のSD
lst_T2_mean = mean (lst_mean_T2), #lst_T2の平均
lst_T2_sd = sd (lst_mean_T2), #lst_T2のSD
aes_T2_mean = mean (aes_mean_T2), #aes_T2の平均
aes_T2_sd = sd (aes_mean_T2), #aes_T2のSD
health_T2_mean = mean (health_mean_T2), #health_T2の平均
health_T2_sd = sd (health_mean_T2), #health_T2のSD
environment_T2_mean = mean (environment_mean_T2), #environment_T2の平均値
environment_T2_sd = sd (environment_mean_T2), #environment_T2のSD
bis_T2_mean = mean (bis_mean_T2), #bis_T2の平均値
bis_T2_sd = sd (bis_mean_T2), #bis_T2のSD
bas_T2_mean = mean (bas_mean_T2), #bas_T2の平均値
bas_T2_sd = sd (bas_mean_T2)) #bas_T2のSD)
knitr::kable(Time2_discriptive_by_gender, digits = 2) #出力
| child_gender_T1 | n | hsc_T2_mean | hsc_T2_sd | eoe_T2_mean | eoe_T2_sd | lst_T2_mean | lst_T2_sd | aes_T2_mean | aes_T2_sd | health_T2_mean | health_T2_sd | environment_T2_mean | environment_T2_sd | bis_T2_mean | bis_T2_sd | bas_T2_mean | bas_T2_sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 147 | 4.43 | 0.73 | 4.35 | 0.97 | 4.17 | 1.12 | 4.76 | 0.89 | 3.86 | 0.93 | 4.37 | 0.85 | 2.51 | 0.44 | 4.95 | 0.67 |
| 1 | 148 | 4.73 | 0.68 | 4.56 | 0.86 | 4.49 | 1.11 | 5.14 | 0.84 | 3.91 | 0.93 | 4.21 | 0.80 | 2.69 | 0.45 | 5.09 | 0.73 |
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(GPArotation)
omega(InputData[,c(11:16, 18:22)],3, fm="ml") #hscT1
## Omega
## Call: omega(m = InputData[, c(11:16, 18:22)], nfactors = 3, fm = "ml")
## Alpha: 0.78
## G.6: 0.81
## Omega Hierarchical: 0.56
## Omega H asymptotic: 0.66
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T1 0.31 0.30 0.20 0.80 0.02
## hsc2_T1 0.44 0.90 1.00 0.00 0.19
## hsc3_T1 0.23 0.49 0.29 0.71 0.17
## hsc4_T1 0.47 0.23 0.30 0.70 0.73
## hsc5_T1 0.26 0.82 0.73 0.27 0.09
## hsc6_T1 0.69 0.26 0.22 0.59 0.41 0.80
## hsc8_T1 0.70 0.31 0.59 0.41 0.83
## hsc9_T1 0.43 0.26 0.74 0.73
## hsc10_T1 0.37 0.64 0.55 0.45 0.24
## hsc11_T1 0.41 0.28 0.25 0.75 0.65
## hsc12_T1 0.52 0.21 0.32 0.68 0.84
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.2 0.3 1.5 1.0
##
## general/max 1.5 max/min = 5.07
## mean percent general = 0.48 with sd = 0.33 and cv of 0.69
## Explained Common Variance of the general factor = 0.44
##
## The degrees of freedom are 25 and the fit is 0.2
## The number of observations was 412 with Chi Square = 82.37 with prob < 4.8e-08
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.076 and the 10 % confidence intervals are 0.057 0.093
## BIC = -68.15
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.24
## The number of observations was 412 with Chi Square = 503.81 with prob < 2.3e-79
## The root mean square of the residuals is 0.14
## The df corrected root mean square of the residuals is 0.16
##
## RMSEA index = 0.161 and the 10 % confidence intervals are 0.147 0.172
## BIC = 238.88
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.84 0.38 0.88 0.96
## Multiple R square of scores with factors 0.70 0.15 0.77 0.92
## Minimum correlation of factor score estimates 0.40 -0.71 0.54 0.85
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.84 0.74 0.71 0.72
## Omega general for total scores and subscales 0.56 0.63 0.10 0.34
## Omega group for total scores and subscales 0.21 0.11 0.61 0.38
omega(InputData[,c(64:69, 71:75)],3, fm="ml") #hscT2
## Omega
## Call: omega(m = InputData[, c(64:69, 71:75)], nfactors = 3, fm = "ml")
## Alpha: 0.78
## G.6: 0.81
## Omega Hierarchical: 0.58
## Omega H asymptotic: 0.69
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T2 0.27 0.10 0.90 0.00
## hsc2_T2 0.41 0.50 0.42 0.58 0.40
## hsc3_T2 0.60 0.39 0.61 0.03
## hsc4_T2 0.59 0.38 0.62 0.92
## hsc5_T2 0.33 0.64 0.54 0.46 0.20
## hsc6_T2 0.88 0.78 0.22 0.99
## hsc8_T2 0.70 0.28 0.57 0.43 0.86
## hsc9_T2 0.39 0.38 0.32 0.68 0.48
## hsc10_T2 0.29 0.69 0.56 0.44 0.15
## hsc11_T2 0.35 0.48 0.36 0.64 0.34
## hsc12_T2 0.60 0.34 0.47 0.53 0.75
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.62 0.91 1.35 0.01
##
## general/max 1.94 max/min = 176.11
## mean percent general = 0.47 with sd = 0.36 and cv of 0.78
## Explained Common Variance of the general factor = 0.54
##
## The degrees of freedom are 25 and the fit is 0.16
## The number of observations was 412 with Chi Square = 64.88 with prob < 2.2e-05
## The root mean square of the residuals is 0.03
## The df corrected root mean square of the residuals is 0.05
## RMSEA index = 0.063 and the 10 % confidence intervals are 0.044 0.081
## BIC = -85.64
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.06
## The number of observations was 412 with Chi Square = 429.74 with prob < 9.9e-65
## The root mean square of the residuals is 0.14
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.147 and the 10 % confidence intervals are 0.134 0.159
## BIC = 164.81
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.92 0.73 0.84 0.07
## Multiple R square of scores with factors 0.84 0.53 0.71 0.00
## Minimum correlation of factor score estimates 0.68 0.06 0.42 -0.99
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.84 0.80 0.75 NA
## Omega general for total scores and subscales 0.58 0.53 0.25 NA
## Omega group for total scores and subscales 0.26 0.26 0.49 NA
omega(InputData[,c(18,16,14,19,22)],3, fm="ml") #eoeT1
## Omega
## Call: omega(m = InputData[, c(18, 16, 14, 19, 22)], nfactors = 3, fm = "ml")
## Alpha: 0.74
## G.6: 0.71
## Omega Hierarchical: 0.66
## Omega H asymptotic: 0.81
## Omega Total 0.82
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc8_T1 0.75 0.63 0.95 0.05 0.59
## hsc6_T1 0.77 0.31 0.69 0.31 0.86
## hsc4_T1 0.43 0.23 0.77 0.79
## hsc9_T1 0.38 0.22 0.20 0.80 0.70
## hsc12_T1 0.53 0.63 0.67 0.33 0.41
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.75 0.43 0.47 0.10
##
## general/max 3.76 max/min = 4.68
## mean percent general = 0.67 with sd = 0.18 and cv of 0.26
## Explained Common Variance of the general factor = 0.64
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.05
## The number of observations was 412 with Chi Square = 21.39 with prob < 0.00068
## The root mean square of the residuals is 0.07
## The df corrected root mean square of the residuals is 0.09
##
## RMSEA index = 0.09 and the 10 % confidence intervals are 0.053 0.13
## BIC = -8.71
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.87 0.79 0.71 0.39
## Multiple R square of scores with factors 0.76 0.62 0.50 0.15
## Minimum correlation of factor score estimates 0.52 0.24 0.01 -0.69
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.82 0.95 0.59 0.69
## Omega general for total scores and subscales 0.66 0.56 0.37 0.59
## Omega group for total scores and subscales 0.12 0.39 0.21 0.10
omega(InputData[,c(71,69,67,72,75)],3, fm="ml") #eoeT2
## Omega
## Call: omega(m = InputData[, c(71, 69, 67, 72, 75)], nfactors = 3, fm = "ml")
## Alpha: 0.8
## G.6: 0.78
## Omega Hierarchical: 0.75
## Omega H asymptotic: 0.89
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc8_T2 0.77 0.41 0.76 0.24 0.77
## hsc6_T2 0.81 0.30 0.74 0.26 0.88
## hsc4_T2 0.59 0.39 0.61 0.90
## hsc9_T2 0.42 0.21 0.24 0.76 0.73
## hsc12_T2 0.65 0.53 0.71 0.29 0.60
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.19 0.35 0.10 0.20
##
## general/max 6.21 max/min = 3.44
## mean percent general = 0.78 with sd = 0.12 and cv of 0.16
## Explained Common Variance of the general factor = 0.77
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.04
## The number of observations was 412 with Chi Square = 15.53 with prob < 0.0083
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## RMSEA index = 0.072 and the 10 % confidence intervals are 0.033 0.113
## BIC = -14.58
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.90 0.66 0.41 0.55
## Multiple R square of scores with factors 0.81 0.43 0.16 0.30
## Minimum correlation of factor score estimates 0.62 -0.13 -0.67 -0.40
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.85 0.67 0.74 0.76
## Omega general for total scores and subscales 0.75 0.52 0.65 0.59
## Omega group for total scores and subscales 0.08 0.15 0.09 0.17
omega(InputData[,c(12,21)],2, fm="ml") #lstT1
##
## Three factors are required for identification -- general factor loadings set to be equal.
## Proceed with caution.
## Think about redoing the analysis with alternative values of the 'option' setting.
## Omega
## Call: omega(m = InputData[, c(12, 21)], nfactors = 2, fm = "ml")
## Alpha: 0.6
## G.6: 0.43
## Omega Hierarchical: 0
## Omega H asymptotic: 0
## Omega Total 0.51
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* h2 u2 p2
## hsc2_T1 0.55 0.3 0.7 0
## hsc11_T1 0.55 0.3 0.7 0
##
## With eigenvalues of:
## g F1* F2*
## 0.00 0.61 0.00
##
## general/max 0 max/min = Inf
## mean percent general = 0 with sd = 0 and cv of NaN
## Explained Common Variance of the general factor = 0
##
## The degrees of freedom are -2 and the fit is 0.02
## The number of observations was 412 with Chi Square = 8.84 with prob < NA
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are -1 and the fit is 0.2
## The number of observations was 412 with Chi Square = 81.96 with prob < NA
## The root mean square of the residuals is 0.43
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2*
## Correlation of scores with factors 0 0.65 0
## Multiple R square of scores with factors 0 0.43 0
## Minimum correlation of factor score estimates -1 -0.15 -1
##
## Total, General and Subset omega for each subset
## g F1* F2*
## Omega total for total scores and subscales 0.51 0.43 NA
## Omega general for total scores and subscales 0.00 0.00 NA
## Omega group for total scores and subscales 0.43 0.43 NA
omega(InputData[,c(65,74)],2, fm="ml") #lstT2
##
## Three factors are required for identification -- general factor loadings set to be equal.
## Proceed with caution.
## Think about redoing the analysis with alternative values of the 'option' setting.
## Omega
## Call: omega(m = InputData[, c(65, 74)], nfactors = 2, fm = "ml")
## Alpha: 0.59
## G.6: 0.42
## Omega Hierarchical: 0
## Omega H asymptotic: 0
## Omega Total 0.5
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* h2 u2 p2
## hsc2_T2 0.54 0.3 0.7 0
## hsc11_T2 0.54 0.3 0.7 0
##
## With eigenvalues of:
## g F1* F2*
## 0.00 0.59 0.00
##
## general/max 0 max/min = Inf
## mean percent general = 0 with sd = 0 and cv of NaN
## Explained Common Variance of the general factor = 0
##
## The degrees of freedom are -2 and the fit is 0.02
## The number of observations was 412 with Chi Square = 8.6 with prob < NA
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are -1 and the fit is 0.19
## The number of observations was 412 with Chi Square = 78.48 with prob < NA
## The root mean square of the residuals is 0.42
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2*
## Correlation of scores with factors 0 0.65 0
## Multiple R square of scores with factors 0 0.42 0
## Minimum correlation of factor score estimates -1 -0.16 -1
##
## Total, General and Subset omega for each subset
## g F1* F2*
## Omega total for total scores and subscales 0.50 0.42 NA
## Omega general for total scores and subscales 0.00 0.00 NA
## Omega group for total scores and subscales 0.42 0.42 NA
omega(InputData[,c(15,20,11,13)],3, fm="ml") #aesT1
## Omega
## Call: omega(m = InputData[, c(15, 20, 11, 13)], nfactors = 3, fm = "ml")
## Alpha: 0.69
## G.6: 0.66
## Omega Hierarchical: 0.7
## Omega H asymptotic: 0.88
## Omega Total 0.79
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc5_T1 0.81 0.59 1.00 0.00 0.65
## hsc10_T1 0.75 0.51 0.82 0.18 0.69
## hsc1_T1 0.29 0.10 0.90 0.83
## hsc3_T1 0.56 0.23 0.37 0.63 0.86
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.62 0.35 0.26 0.06
##
## general/max 4.64 max/min = 5.54
## mean percent general = 0.76 with sd = 0.1 and cv of 0.14
## Explained Common Variance of the general factor = 0.71
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.01
## The number of observations was 412 with Chi Square = 2.47 with prob < 0.29
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.04
##
## RMSEA index = 0.024 and the 10 % confidence intervals are 0 0.104
## BIC = -9.57
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.77 0.65 0.28
## Multiple R square of scores with factors 0.79 0.59 0.43 0.08
## Minimum correlation of factor score estimates 0.57 0.19 -0.14 -0.85
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.79 0.99 0.82 0.35
## Omega general for total scores and subscales 0.70 0.65 0.57 0.31
## Omega group for total scores and subscales 0.09 0.34 0.26 0.05
omega(InputData[,c(68,73,64,66)],3, fm="ml") #aesT2
## Omega
## Call: omega(m = InputData[, c(68, 73, 64, 66)], nfactors = 3, fm = "ml")
## Alpha: 0.65
## G.6: 0.62
## Omega Hierarchical: 0.61
## Omega H asymptotic: 0.77
## Omega Total 0.79
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc5_T2 0.87 0.78 0.22 0.98
## hsc10_T2 0.63 0.75 0.96 0.04 0.42
## hsc1_T2 0.24 0.44 0.25 0.75 0.22
## hsc3_T2 0.44 0.22 0.31 0.34 0.66 0.58
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.41 0.61 0.01 0.29
##
## general/max 2.31 max/min = 42.13
## mean percent general = 0.55 with sd = 0.32 and cv of 0.59
## Explained Common Variance of the general factor = 0.61
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.08
## The number of observations was 412 with Chi Square = 30.97 with prob < 1.9e-07
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.188 and the 10 % confidence intervals are 0.133 0.249
## BIC = 18.93
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.9 0.90 0.15 0.53
## Multiple R square of scores with factors 0.8 0.81 0.02 0.28
## Minimum correlation of factor score estimates 0.6 0.63 -0.96 -0.44
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.79 0.96 0.78 0.42
## Omega general for total scores and subscales 0.61 0.40 0.76 0.19
## Omega group for total scores and subscales 0.15 0.56 0.01 0.23
omega(InputData[,23:27],3, fm="ml") #mentalT1
## Omega
## Call: omega(m = InputData[, 23:27], nfactors = 3, fm = "ml")
## Alpha: 0.88
## G.6: 0.87
## Omega Hierarchical: 0.81
## Omega H asymptotic: 0.87
## Omega Total 0.93
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## health1_T1 0.93 0.37 0.99 0.01 0.86
## health2_T1 0.87 0.49 0.99 0.01 0.76
## health3_T1 0.63 0.37 0.54 0.46 0.74
## health4_T1 0.63 0.26 0.50 0.50 0.81
## health5_T1 0.64 0.55 0.71 0.29 0.58
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.83 0.51 0.27 0.14
##
## general/max 5.6 max/min = 3.61
## mean percent general = 0.75 with sd = 0.11 and cv of 0.14
## Explained Common Variance of the general factor = 0.76
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.19
## The number of observations was 412 with Chi Square = 76.09 with prob < 5.5e-15
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.187 and the 10 % confidence intervals are 0.15 0.224
## BIC = 45.99
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.95 0.74 0.84 0.66
## Multiple R square of scores with factors 0.91 0.55 0.71 0.44
## Minimum correlation of factor score estimates 0.82 0.09 0.42 -0.13
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.93 0.80 0.99 0.99
## Omega general for total scores and subscales 0.81 0.58 0.76 0.86
## Omega group for total scores and subscales 0.10 0.22 0.24 0.14
omega(InputData[,76:80],3, fm="ml") #mentalT2
## Omega
## Call: omega(m = InputData[, 76:80], nfactors = 3, fm = "ml")
## Alpha: 0.89
## G.6: 0.88
## Omega Hierarchical: 0.81
## Omega H asymptotic: 0.88
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## health1_T2 0.75 0.49 0.81 0.19 0.70
## health2_T2 0.72 0.56 0.86 0.14 0.61
## health3_T2 0.74 0.26 -0.21 0.65 0.35 0.84
## health4_T2 0.65 0.27 0.51 0.49 0.84
## health5_T2 0.87 0.77 0.23 0.99
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.81 0.63 0.01 0.15
##
## general/max 4.46 max/min = 100.94
## mean percent general = 0.8 with sd = 0.15 and cv of 0.18
## Explained Common Variance of the general factor = 0.78
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.42
## The number of observations was 412 with Chi Square = 170.97 with prob < 4.5e-35
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.285 and the 10 % confidence intervals are 0.249 0.322
## BIC = 140.86
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.92 0.77 0.09 0.54
## Multiple R square of scores with factors 0.86 0.59 0.01 0.29
## Minimum correlation of factor score estimates 0.71 0.18 -0.98 -0.41
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.92 0.91 NA 0.76
## Omega general for total scores and subscales 0.81 0.67 NA 0.75
## Omega group for total scores and subscales 0.10 0.24 NA 0.01
omega(InputData[,c(29,31,33,35,37,39,41,43)],3, fm="ml") #positiveT1
## Omega
## Call: omega(m = InputData[, c(29, 31, 33, 35, 37, 39, 41, 43)], nfactors = 3,
## fm = "ml")
## Alpha: 0.84
## G.6: 0.83
## Omega Hierarchical: 0.72
## Omega H asymptotic: 0.83
## Omega Total 0.87
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## panas2_T1 0.67 0.39 0.61 0.39 0.74
## panas4_T1 0.65 0.51 0.49 0.83
## panas6_T1 0.63 0.54 0.69 0.31 0.57
## panas8_T1 0.59 0.42 0.58 0.84
## panas10_T1 0.54 0.30 0.40 0.60 0.73
## panas12_T1 0.64 0.31 0.53 0.47 0.78
## panas14_T1 0.47 0.55 0.53 0.47 0.42
## panas16_T1 0.46 0.21 0.28 0.72 0.75
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.76 0.32 0.50 0.38
##
## general/max 5.48 max/min = 1.57
## mean percent general = 0.71 with sd = 0.14 and cv of 0.2
## Explained Common Variance of the general factor = 0.7
##
## The degrees of freedom are 7 and the fit is 0.02
## The number of observations was 412 with Chi Square = 6.4 with prob < 0.49
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.057
## BIC = -35.75
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.17
## The number of observations was 412 with Chi Square = 68.46 with prob < 3.2e-07
## The root mean square of the residuals is 0.07
## The df corrected root mean square of the residuals is 0.09
##
## RMSEA index = 0.077 and the 10 % confidence intervals are 0.057 0.097
## BIC = -51.96
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.86 0.48 0.64 0.63
## Multiple R square of scores with factors 0.74 0.23 0.41 0.39
## Minimum correlation of factor score estimates 0.47 -0.53 -0.18 -0.22
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.87 0.74 0.64 0.72
## Omega general for total scores and subscales 0.72 0.62 0.42 0.54
## Omega group for total scores and subscales 0.08 0.13 0.22 0.18
omega(InputData[,c(28,30,32,34,36,38,40,42)],3, fm="ml") #negativeT1
## Omega
## Call: omega(m = InputData[, c(28, 30, 32, 34, 36, 38, 40, 42)], nfactors = 3,
## fm = "ml")
## Alpha: 0.86
## G.6: 0.88
## Omega Hierarchical: 0.64
## Omega H asymptotic: 0.71
## Omega Total 0.91
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## panas1_T1 0.80 0.55 0.95 0.05 0.68
## panas3_T1 0.77 0.35 0.24 0.76 0.24 0.77
## panas5_T1 0.70 0.44 0.70 0.30 0.70
## panas7_T1 0.47 0.46 0.45 0.55 0.48
## panas9_T1 0.50 0.53 0.54 0.46 0.47
## panas11_T1 0.44 0.63 0.58 0.42 0.33
## panas13_T1 0.47 0.23 0.21 0.32 0.68 0.69
## panas15_T1 0.45 0.61 0.58 0.42 0.35
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.79 1.32 0.44 0.34
##
## general/max 2.12 max/min = 3.92
## mean percent general = 0.56 with sd = 0.17 and cv of 0.31
## Explained Common Variance of the general factor = 0.57
##
## The degrees of freedom are 7 and the fit is 0.02
## The number of observations was 412 with Chi Square = 9.91 with prob < 0.19
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.033 and the 10 % confidence intervals are 0 0.073
## BIC = -32.24
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.96
## The number of observations was 412 with Chi Square = 390.14 with prob < 2.3e-70
## The root mean square of the residuals is 0.16
## The df corrected root mean square of the residuals is 0.19
##
## RMSEA index = 0.213 and the 10 % confidence intervals are 0.194 0.231
## BIC = 269.72
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.87 0.81 0.67 0.64
## Multiple R square of scores with factors 0.75 0.65 0.46 0.41
## Minimum correlation of factor score estimates 0.50 0.30 -0.09 -0.17
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.91 0.81 0.91 0.68
## Omega general for total scores and subscales 0.64 0.38 0.68 0.49
## Omega group for total scores and subscales 0.21 0.43 0.22 0.20
omega(InputData[,53:63],3, fm="ml") #environmentT2
## Omega
## Call: omega(m = InputData[, 53:63], nfactors = 3, fm = "ml")
## Alpha: 0.86
## G.6: 0.91
## Omega Hierarchical: 0.51
## Omega H asymptotic: 0.56
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## environment1_T2 0.52 0.66 0.71 0.29 0.38
## environment2_T2 0.56 0.70 0.82 0.18 0.39
## environment3_T2 0.48 0.50 0.50 0.50 0.47
## environment4_T2 0.46 0.46 0.44 0.56 0.47
## environment5_T2 0.49 0.49 0.50 0.50 0.48
## environment6_T2 0.54 0.57 0.62 0.38 0.47
## environment7_T2 0.44 0.74 0.74 0.26 0.26
## environment8_T2 0.46 0.65 0.63 0.37 0.33
## environment9_T2 0.35 0.75 0.69 0.31 0.17
## environment10_T2 0.40 0.89 0.95 0.05 0.16
## environment11_T2 0.41 0.34 0.32 0.68 0.54
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.4 2.1 1.4 1.0
##
## general/max 1.16 max/min = 2.03
## mean percent general = 0.37 with sd = 0.13 and cv of 0.35
## Explained Common Variance of the general factor = 0.35
##
## The degrees of freedom are 25 and the fit is 0.5
## The number of observations was 412 with Chi Square = 201.16 with prob < 1.8e-29
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.132 and the 10 % confidence intervals are 0.114 0.148
## BIC = 50.64
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 3.16
## The number of observations was 412 with Chi Square = 1281.83 with prob < 8e-240
## The root mean square of the residuals is 0.22
## The df corrected root mean square of the residuals is 0.25
##
## RMSEA index = 0.263 and the 10 % confidence intervals are 0.249 0.274
## BIC = 1016.91
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.73 0.78 0.93 0.81
## Multiple R square of scores with factors 0.53 0.61 0.87 0.66
## Minimum correlation of factor score estimates 0.06 0.21 0.73 0.32
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.92 0.88 0.90 0.81
## Omega general for total scores and subscales 0.51 0.41 0.15 0.24
## Omega group for total scores and subscales 0.37 0.47 0.75 0.57
omega(InputData[,c(101,86,91,93,95,102)],3, fm="ml") #bisT2
## Omega
## Call: omega(m = InputData[, c(101, 86, 91, 93, 95, 102)], nfactors = 3,
## fm = "ml")
## Alpha: 0.64
## G.6: 0.65
## Omega Hierarchical: 0.65
## Omega H asymptotic: 0.85
## Omega Total 0.76
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## bis1_T2 0.35 0.55 0.42 0.58 0.30
## bis2_T2 0.53 0.31 0.69 0.92
## bas8_T2 0.82 0.71 0.29 0.04
## bis4_T2 0.67 0.47 0.53 0.95
## bis5_T2 0.84 0.72 0.28 0.99
## bis6_T2 0.32 0.38 0.32 0.68 0.32
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.70 0.01 0.48 0.73
##
## general/max 2.32 max/min = 113.66
## mean percent general = 0.59 with sd = 0.41 and cv of 0.71
## Explained Common Variance of the general factor = 0.58
##
## The degrees of freedom are 0 and the fit is 0
## The number of observations was 412 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 9 and the fit is 0.18
## The number of observations was 412 with Chi Square = 72.31 with prob < 5.4e-12
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.131 and the 10 % confidence intervals are 0.104 0.16
## BIC = 18.12
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.07 0.65 0.84
## Multiple R square of scores with factors 0.80 0.00 0.42 0.71
## Minimum correlation of factor score estimates 0.59 -0.99 -0.16 0.41
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.76 0.72 0.62 0.60
## Omega general for total scores and subscales 0.65 0.71 0.37 0.21
## Omega group for total scores and subscales 0.17 0.00 0.24 0.40
omega(InputData[,c(82,83,84,85,87,88,89,91,92,94,96,97,99)],3, fm="ml") #basT2
## Omega
## Call: omega(m = InputData[, c(82, 83, 84, 85, 87, 88, 89, 91, 92, 94,
## 96, 97, 99)], nfactors = 3, fm = "ml")
## Alpha: 0.83
## G.6: 0.85
## Omega Hierarchical: 0.58
## Omega H asymptotic: 0.68
## Omega Total 0.86
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## bas1_T2 0.63 0.36 0.54 0.46 0.75
## bas2_T2 0.37 0.64 0.55 0.45 0.24
## bas3_T2 0.55 0.29 0.43 0.57 0.70
## bas4_T2 0.52 0.49 0.52 0.48 0.52
## bas5_T2 0.73 0.44 0.73 0.27 0.74
## bas6_T2 0.38 0.21 0.24 0.76 0.62
## bas7_T2 0.59 0.29 0.44 0.56 0.77
## bas8_T2 0.46 0.27 0.32 0.39 0.61 0.54
## bas9_T2 0.28 0.11 0.89 0.26
## bas10_T2 0.30 0.33 0.22 0.25 0.75 0.37
## bas11_T2 0.27 0.70 0.57 0.43 0.13
## bas12_T2 0.38 0.32 0.26 0.74 0.55
## bas13_T2 0.33 0.34 0.36 0.35 0.65 0.31
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.80 0.47 1.07 1.04
##
## general/max 2.61 max/min = 2.28
## mean percent general = 0.5 with sd = 0.22 and cv of 0.43
## Explained Common Variance of the general factor = 0.52
##
## The degrees of freedom are 42 and the fit is 0.33
## The number of observations was 412 with Chi Square = 132.86 with prob < 2.3e-11
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.073 and the 10 % confidence intervals are 0.059 0.087
## BIC = -120.03
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 65 and the fit is 1.11
## The number of observations was 412 with Chi Square = 450.84 with prob < 4.2e-59
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.121 and the 10 % confidence intervals are 0.11 0.131
## BIC = 59.48
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.83 0.50 0.76 0.79
## Multiple R square of scores with factors 0.69 0.25 0.58 0.62
## Minimum correlation of factor score estimates 0.39 -0.50 0.17 0.24
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.86 0.80 0.72 0.66
## Omega general for total scores and subscales 0.58 0.61 0.36 0.30
## Omega group for total scores and subscales 0.16 0.19 0.36 0.36
#相関表1列目
cor.test(InputData$hsc_mean_T1, InputData$hsc_mean_T2, method = "pearson", digits = 2) #hscT1-hscT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$hsc_mean_T2
## t = 8.923, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3446553 0.5165641
## sample estimates:
## cor
## 0.4345594
cor.test(InputData$hsc_mean_T1, InputData$eoe_mean_T1, method = "pearson") #hscT1-eoeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$eoe_mean_T1
## t = 24.52, df = 410, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7287504 0.8075291
## sample estimates:
## cor
## 0.771074
cor.test(InputData$hsc_mean_T1, InputData$eoe_mean_T2, method = "pearson") #hscT1-eoeT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$eoe_mean_T2
## t = 7.2916, df = 342, p-value = 2.144e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2715963 0.4549006
## sample estimates:
## cor
## 0.3668033
cor.test(InputData$hsc_mean_T1, InputData$lst_mean_T1, method = "pearson") #hscT1-lstT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$lst_mean_T1
## t = 27.622, df = 410, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7698866 0.8378388
## sample estimates:
## cor
## 0.80651
cor.test(InputData$hsc_mean_T1, InputData$lst_mean_T2, method = "pearson") #hscT1-lstT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$lst_mean_T2
## t = 6.2898, df = 342, p-value = 9.713e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2238806 0.4136558
## sample estimates:
## cor
## 0.321999
cor.test(InputData$hsc_mean_T1, InputData$aes_mean_T1, method = "pearson") #hscT1-aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$aes_mean_T1
## t = 18.04, df = 410, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6076562 0.7158228
## sample estimates:
## cor
## 0.6652153
cor.test(InputData$hsc_mean_T1, InputData$aes_mean_T2, method = "pearson") #hscT1-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$aes_mean_T2
## t = 5.3042, df = 342, p-value = 2.037e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1750647 0.3706382
## sample estimates:
## cor
## 0.2757022
cor.test(InputData$hsc_mean_T1, InputData$health_mean_T1, method = "pearson") #hscT1-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$health_mean_T1
## t = -2.5084, df = 410, p-value = 0.01251
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.21697715 -0.02664745
## sample estimates:
## cor
## -0.1229426
cor.test(InputData$hsc_mean_T1, InputData$health_mean_T2, method = "pearson") #hscT1-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$health_mean_T2
## t = -0.30357, df = 342, p-value = 0.7616
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.12194261 0.08948352
## sample estimates:
## cor
## -0.01641302
cor.test(InputData$hsc_mean_T1, InputData$positive_mean_T1, method = "pearson") #hscT1-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$positive_mean_T1
## t = -0.22525, df = 410, p-value = 0.8219
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10761973 0.08557999
## sample estimates:
## cor
## -0.01112368
cor.test(InputData$hsc_mean_T1, InputData$negative_mean_T1, method = "pearson") #hscT1-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$negative_mean_T1
## t = 4.5521, df = 410, p-value = 7.012e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1253811 0.3093915
## sample estimates:
## cor
## 0.2193359
cor.test(InputData$hsc_mean_T1, InputData$environment_mean_T2, method = "pearson") #hscT1-environmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$environment_mean_T2
## t = 0.23223, df = 342, p-value = 0.8165
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09330843 0.11814106
## sample estimates:
## cor
## 0.01255669
cor.test(InputData$hsc_mean_T1, InputData$bis_mean_T2, method = "pearson") #hscT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$bis_mean_T2
## t = 6.7614, df = 342, p-value = 5.922e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2465949 0.4333875
## sample estimates:
## cor
## 0.3433824
cor.test(InputData$hsc_mean_T1, InputData$bas_mean_T2, method = "pearson") #hscT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T1 and InputData$bas_mean_T2
## t = 2.2993, df = 342, p-value = 0.02209
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01787342 0.22617194
## sample estimates:
## cor
## 0.1233815
#相関表2列目
cor.test(InputData$hsc_mean_T2, InputData$eoe_mean_T1, method = "pearson") #hscT2-eoeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$eoe_mean_T1
## t = 7.376, df = 342, p-value = 1.244e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2755198 0.4582575
## sample estimates:
## cor
## 0.3704679
cor.test(InputData$hsc_mean_T2, InputData$eoe_mean_T2, method = "pearson") #hscT2-eoeT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$eoe_mean_T2
## t = 24.763, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7598584 0.8361255
## sample estimates:
## cor
## 0.8012227
cor.test(InputData$hsc_mean_T2, InputData$lst_mean_T1, method = "pearson") #hscT2-lstT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$lst_mean_T1
## t = 7.5374, df = 342, p-value = 4.339e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2829851 0.4646301
## sample estimates:
## cor
## 0.3774323
cor.test(InputData$hsc_mean_T2, InputData$lst_mean_T2, method = "pearson") #hscT2-lstT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$lst_mean_T2
## t = 25.226, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7660801 0.8405503
## sample estimates:
## cor
## 0.8064906
cor.test(InputData$hsc_mean_T2, InputData$aes_mean_T1, method = "pearson") #hscT2-aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$aes_mean_T1
## t = 4.3485, df = 342, p-value = 1.811e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1262086 0.3267283
## sample estimates:
## cor
## 0.2288951
cor.test(InputData$hsc_mean_T2, InputData$aes_mean_T2, method = "pearson") #hscT2-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$aes_mean_T2
## t = 13.741, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5237115 0.6605092
## sample estimates:
## cor
## 0.5964241
cor.test(InputData$hsc_mean_T2, InputData$health_mean_T1, method = "pearson") #hscT2-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$health_mean_T1
## t = -2.4357, df = 342, p-value = 0.01537
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23310489 -0.02518894
## sample estimates:
## cor
## -0.1305824
cor.test(InputData$hsc_mean_T2, InputData$health_mean_T2, method = "pearson") #hscT2-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$health_mean_T2
## t = -0.88858, df = 342, p-value = 0.3749
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15295873 0.05804204
## sample estimates:
## cor
## -0.04799375
cor.test(InputData$hsc_mean_T2, InputData$positive_mean_T1, method = "pearson") #hscT2-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$positive_mean_T1
## t = -2.3711, df = 342, p-value = 0.01829
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.22982210 -0.02172222
## sample estimates:
## cor
## -0.1271713
cor.test(InputData$hsc_mean_T2, InputData$negative_mean_T1, method = "pearson") #hscT2-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$negative_mean_T1
## t = 4.9709, df = 342, p-value = 1.056e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1581808 0.3555619
## sample estimates:
## cor
## 0.2595802
cor.test(InputData$hsc_mean_T2, InputData$environment_mean_T2, method = "pearson") #hscT2-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$environment_mean_T2
## t = 0.53528, df = 342, p-value = 0.5928
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07704435 0.13426306
## sample estimates:
## cor
## 0.02893259
cor.test(InputData$hsc_mean_T2, InputData$bis_mean_T2, method = "pearson") #hscT2-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$bis_mean_T2
## t = 9.9035, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3855968 0.5503571
## sample estimates:
## cor
## 0.4720893
cor.test(InputData$hsc_mean_T2, InputData$bas_mean_T2, method = "pearson") #hscT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$hsc_mean_T2 and InputData$bas_mean_T2
## t = 2.571, df = 342, p-value = 0.01056
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03243059 0.23994709
## sample estimates:
## cor
## 0.1376996
#相関表3列目
cor.test(InputData$eoe_mean_T1, InputData$eoe_mean_T2, method = "pearson") #eoeT1-eoeT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$eoe_mean_T2
## t = 10.476, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4084428 0.5689824
## sample estimates:
## cor
## 0.4928962
cor.test(InputData$eoe_mean_T1, InputData$lst_mean_T1, method = "pearson") #eoeT1-lstT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$lst_mean_T1
## t = 11.669, df = 410, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4231176 0.5685052
## sample estimates:
## cor
## 0.4993182
cor.test(InputData$eoe_mean_T1, InputData$lst_mean_T2, method = "pearson") #eoeT1-lstT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$lst_mean_T2
## t = 5.2838, df = 342, p-value = 2.258e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1740362 0.3697228
## sample estimates:
## cor
## 0.2747218
cor.test(InputData$eoe_mean_T1, InputData$aes_mean_T1, method = "pearson") #eoeT1-aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$aes_mean_T1
## t = 5.9861, df = 410, p-value = 4.695e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1921554 0.3699820
## sample estimates:
## cor
## 0.283504
cor.test(InputData$eoe_mean_T1, InputData$aes_mean_T2, method = "pearson") #eoeT1-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$aes_mean_T2
## t = 0.98851, df = 342, p-value = 0.3236
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05266207 0.15822458
## sample estimates:
## cor
## 0.05337639
cor.test(InputData$eoe_mean_T1, InputData$health_mean_T1, method = "pearson") #eoeT1-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$health_mean_T1
## t = -5.5641, df = 410, p-value = 4.765e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3525565 -0.1727813
## sample estimates:
## cor
## -0.26497
cor.test(InputData$eoe_mean_T1, InputData$health_mean_T2, method = "pearson") #eoeT1-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$health_mean_T2
## t = -3.849, df = 342, p-value = 0.0001415
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3029749 -0.1001788
## sample estimates:
## cor
## -0.2037615
cor.test(InputData$eoe_mean_T1, InputData$positive_mean_T1, method = "pearson") #eoeT1-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$positive_mean_T1
## t = -3.7778, df = 410, p-value = 0.0001816
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.27514471 -0.08836243
## sample estimates:
## cor
## -0.1834084
cor.test(InputData$eoe_mean_T1, InputData$negative_mean_T1, method = "pearson") #eoeT1-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$negative_mean_T1
## t = 7.5338, df = 410, p-value = 3.181e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2608886 0.4308090
## sample estimates:
## cor
## 0.3487111
cor.test(InputData$eoe_mean_T1, InputData$environment_mean_T2, method = "pearson") #eoeT1-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$environment_mean_T2
## t = -1.2305, df = 342, p-value = 0.2194
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.17093250 0.03962816
## sample estimates:
## cor
## -0.06639127
cor.test(InputData$eoe_mean_T1, InputData$bis_mean_T2, method = "pearson") #eoeT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$bis_mean_T2
## t = 6.3893, df = 342, p-value = 5.451e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2287083 0.4178646
## sample estimates:
## cor
## 0.3265522
cor.test(InputData$eoe_mean_T1, InputData$bas_mean_T2, method = "pearson") #eoeT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T1 and InputData$bas_mean_T2
## t = -0.1868, df = 342, p-value = 0.8519
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11571807 0.09574305
## sample estimates:
## cor
## -0.01010043
#相関表4列目
cor.test(InputData$eoe_mean_T2, InputData$lst_mean_T1, method = "pearson") #eoeT2-lstT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$lst_mean_T1
## t = 5.5222, df = 342, p-value = 6.634e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1860099 0.3803569
## sample estimates:
## cor
## 0.2861234
cor.test(InputData$eoe_mean_T2, InputData$lst_mean_T2, method = "pearson") #eoeT2-lstT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$lst_mean_T2
## t = 11.987, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4648980 0.6143121
## sample estimates:
## cor
## 0.5439016
cor.test(InputData$eoe_mean_T2, InputData$aes_mean_T1, method = "pearson") #eoeT2-aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$aes_mean_T1
## t = 1.2416, df = 342, p-value = 0.2153
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03903279 0.17151131
## sample estimates:
## cor
## 0.06698491
cor.test(InputData$eoe_mean_T2, InputData$aes_mean_T2, method = "pearson") #eoeT2-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$aes_mean_T2
## t = 4.6557, df = 342, p-value = 4.627e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1420609 0.3410713
## sample estimates:
## cor
## 0.2441349
cor.test(InputData$eoe_mean_T2, InputData$health_mean_T1, method = "pearson") #eoeT2-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$health_mean_T1
## t = -5.609, df = 342, p-value = 4.202e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3841959 -0.1903467
## sample estimates:
## cor
## -0.290246
cor.test(InputData$eoe_mean_T2, InputData$health_mean_T2, method = "pearson") #eoeT2-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$health_mean_T2
## t = -4.8613, df = 342, p-value = 1.781e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3505477 -0.1525909
## sample estimates:
## cor
## -0.2542301
cor.test(InputData$eoe_mean_T2, InputData$positive_mean_T1, method = "pearson") #eoeT2-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$positive_mean_T1
## t = -5.0311, df = 342, p-value = 7.893e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3583061 -0.1612454
## sample estimates:
## cor
## -0.2625108
cor.test(InputData$eoe_mean_T2, InputData$negative_mean_T1, method = "pearson") #eoeT2-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$negative_mean_T1
## t = 6.5213, df = 342, p-value = 2.506e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2350861 0.4234122
## sample estimates:
## cor
## 0.3325604
cor.test(InputData$eoe_mean_T2, InputData$environment_mean_T2, method = "pearson") #eoeT2-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$environment_mean_T2
## t = -1.9173, df = 342, p-value = 0.05604
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.206610185 0.002648227
## sample estimates:
## cor
## -0.1031219
cor.test(InputData$eoe_mean_T2, InputData$bis_mean_T2, method = "pearson") #eoeT2-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$bis_mean_T2
## t = 11.002, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4287277 0.5853829
## sample estimates:
## cor
## 0.51129
cor.test(InputData$eoe_mean_T2, InputData$bas_mean_T2, method = "pearson") #eoeT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$eoe_mean_T2 and InputData$bas_mean_T2
## t = -0.708, df = 342, p-value = 0.4794
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14341728 0.06775913
## sample estimates:
## cor
## -0.03825621
#相関表5列目
cor.test(InputData$lst_mean_T1, InputData$lst_mean_T2, method = "pearson") #lstT1-lstT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$lst_mean_T2
## t = 8.6592, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3332523 0.5070556
## sample estimates:
## cor
## 0.4240506
cor.test(InputData$lst_mean_T1, InputData$aes_mean_T1, method = "pearson") #lstT1-aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$aes_mean_T1
## t = 5.0122, df = 410, p-value = 8.019e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1470872 0.3292527
## sample estimates:
## cor
## 0.2402843
cor.test(InputData$lst_mean_T1, InputData$aes_mean_T2, method = "pearson") #lstT1-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$aes_mean_T2
## t = 1.7323, df = 342, p-value = 0.08412
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01260125 0.19706208
## sample estimates:
## cor
## 0.09326425
cor.test(InputData$lst_mean_T1, InputData$health_mean_T1, method = "pearson") #lstT1-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$health_mean_T1
## t = -2.2038, df = 410, p-value = 0.0281
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.20268988 -0.01170759
## sample estimates:
## cor
## -0.1081969
cor.test(InputData$lst_mean_T1, InputData$health_mean_T2, method = "pearson") #lstT1-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$health_mean_T2
## t = -0.80699, df = 342, p-value = 0.4202
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14865131 0.06243371
## sample estimates:
## cor
## -0.04359533
cor.test(InputData$lst_mean_T1, InputData$positive_mean_T1, method = "pearson") #lstT1-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$positive_mean_T1
## t = -1.8642, df = 410, p-value = 0.06301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.186636754 0.004977662
## sample estimates:
## cor
## -0.09167813
cor.test(InputData$lst_mean_T1, InputData$negative_mean_T1, method = "pearson") #lstT1-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$negative_mean_T1
## t = 4.4467, df = 410, p-value = 1.124e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1203790 0.3047916
## sample estimates:
## cor
## 0.2144961
cor.test(InputData$lst_mean_T1, InputData$environment_mean_T2, method = "pearson") #lstT1-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$environment_mean_T2
## t = 0.034699, df = 342, p-value = 0.9723
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1038856 0.1075962
## sample estimates:
## cor
## 0.001876282
cor.test(InputData$lst_mean_T1, InputData$bis_mean_T2, method = "pearson") #lstT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$bis_mean_T2
## t = 5.5691, df = 342, p-value = 5.187e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1883547 0.3824334
## sample estimates:
## cor
## 0.2883529
cor.test(InputData$lst_mean_T1, InputData$bas_mean_T2, method = "pearson") #lstT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T1 and InputData$bas_mean_T2
## t = -0.38644, df = 342, p-value = 0.6994
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.12635381 0.08503738
## sample estimates:
## cor
## -0.02089171
#相関表6列目
cor.test(InputData$lst_mean_T2, InputData$aes_mean_T1, method = "pearson") #lstT2-aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$aes_mean_T1
## t = 0.060947, df = 342, p-value = 0.9514
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1024813 0.1089989
## sample estimates:
## cor
## 0.003295619
cor.test(InputData$lst_mean_T2, InputData$aes_mean_T2, method = "pearson") #lstT2-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$aes_mean_T2
## t = 2.8228, df = 342, p-value = 0.005039
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04588355 0.25260348
## sample estimates:
## cor
## 0.1508927
cor.test(InputData$lst_mean_T2, InputData$health_mean_T1, method = "pearson") #lstT2-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$health_mean_T1
## t = -2.2863, df = 342, p-value = 0.02285
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.22550938 -0.01717547
## sample estimates:
## cor
## -0.1226939
cor.test(InputData$lst_mean_T2, InputData$health_mean_T2, method = "pearson") #lstT2-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$health_mean_T2
## t = -2.2124, df = 342, p-value = 0.0276
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.22174015 -0.01320873
## sample estimates:
## cor
## -0.1187841
cor.test(InputData$lst_mean_T2, InputData$positive_mean_T1, method = "pearson") #lstT2-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$positive_mean_T1
## t = -3.5651, df = 342, p-value = 0.0004155
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.28924501 -0.08525866
## sample estimates:
## cor
## -0.1892933
cor.test(InputData$lst_mean_T2, InputData$negative_mean_T1, method = "pearson") #lstT2-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$negative_mean_T1
## t = 4.0565, df = 342, p-value = 6.175e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1110314 0.3129091
## sample estimates:
## cor
## 0.2142571
cor.test(InputData$lst_mean_T2, InputData$environment_mean_T2, method = "pearson") #lstT2-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$environment_mean_T2
## t = -0.9434, df = 342, p-value = 0.3461
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15584883 0.05509083
## sample estimates:
## cor
## -0.05094719
cor.test(InputData$lst_mean_T2, InputData$bis_mean_T2, method = "pearson") #lstT2-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$bis_mean_T2
## t = 7.0871, df = 342, p-value = 7.876e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2620218 0.4466872
## sample estimates:
## cor
## 0.3578483
cor.test(InputData$lst_mean_T2, InputData$bas_mean_T2, method = "pearson") #lstT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$lst_mean_T2 and InputData$bas_mean_T2
## t = -0.26177, df = 342, p-value = 0.7937
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11971543 0.09172514
## sample estimates:
## cor
## -0.01415337
#相関表7列目
cor.test(InputData$aes_mean_T1, InputData$aes_mean_T2, method = "pearson") #aesT1-aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$aes_mean_T2
## t = 10.073, df = 342, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3924352 0.5559493
## sample estimates:
## cor
## 0.4783274
cor.test(InputData$aes_mean_T1, InputData$health_mean_T1, method = "pearson") #aesT1-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$health_mean_T1
## t = 1.7328, df = 410, p-value = 0.08388
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01143855 0.18039304
## sample estimates:
## cor
## 0.08526738
cor.test(InputData$aes_mean_T1, InputData$health_mean_T2, method = "pearson") #aesT1-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$health_mean_T2
## t = 3.7233, df = 342, p-value = 0.0002299
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0935857 0.2969181
## sample estimates:
## cor
## 0.1973737
cor.test(InputData$aes_mean_T1, InputData$positive_mean_T1, method = "pearson") #aesT1-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$positive_mean_T1
## t = 5.2306, df = 410, p-value = 2.7e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1573009 0.3385428
## sample estimates:
## cor
## 0.2501116
cor.test(InputData$aes_mean_T1, InputData$negative_mean_T1, method = "pearson") #aesT1-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$negative_mean_T1
## t = -1.2825, df = 410, p-value = 0.2004
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15885114 0.03360743
## sample estimates:
## cor
## -0.0632095
cor.test(InputData$aes_mean_T1, InputData$environment_mean_T2, method = "pearson") #aesT1-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$environment_mean_T2
## t = 1.6148, df = 342, p-value = 0.1073
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01892875 0.19097125
## sample estimates:
## cor
## 0.08698659
cor.test(InputData$aes_mean_T1, InputData$bis_mean_T2, method = "pearson") #aesT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$bis_mean_T2
## t = 3.0274, df = 342, p-value = 0.002654
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05678109 0.26280416
## sample estimates:
## cor
## 0.1615523
cor.test(InputData$aes_mean_T1, InputData$bas_mean_T2, method = "pearson") #aesT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T1 and InputData$bas_mean_T2
## t = 6.1805, df = 342, p-value = 1.818e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2185529 0.4090018
## sample estimates:
## cor
## 0.316969
#相関表8列目
cor.test(InputData$aes_mean_T2, InputData$health_mean_T1, method = "pearson") #aesT2-healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$health_mean_T1
## t = 2.4345, df = 342, p-value = 0.01542
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02512032 0.23303997
## sample estimates:
## cor
## 0.1305149
cor.test(InputData$aes_mean_T2, InputData$health_mean_T2, method = "pearson") #aesT2-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$health_mean_T2
## t = 5.5576, df = 342, p-value = 5.512e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1877788 0.3819235
## sample estimates:
## cor
## 0.2878053
cor.test(InputData$aes_mean_T2, InputData$positive_mean_T1, method = "pearson") #aesT2-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$positive_mean_T1
## t = 3.6434, df = 342, p-value = 0.0003108
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08938225 0.29304806
## sample estimates:
## cor
## 0.1932966
cor.test(InputData$aes_mean_T2, InputData$negative_mean_T1, method = "pearson") #aesT2-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$negative_mean_T1
## t = 0.43148, df = 342, p-value = 0.6664
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08261982 0.12874887
## sample estimates:
## cor
## 0.02332519
cor.test(InputData$aes_mean_T2, InputData$environment_mean_T2, method = "pearson") #aesT2-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$environment_mean_T2
## t = 4.5, df = 342, p-value = 9.328e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1340418 0.3338272
## sample estimates:
## cor
## 0.2364319
cor.test(InputData$aes_mean_T2, InputData$bis_mean_T2, method = "pearson") #aesT2-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$bis_mean_T2
## t = 3.3062, df = 342, p-value = 0.001046
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07157754 0.27658109
## sample estimates:
## cor
## 0.1759868
cor.test(InputData$aes_mean_T2, InputData$bas_mean_T2, method = "pearson") #aesT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$aes_mean_T2 and InputData$bas_mean_T2
## t = 7.7575, df = 342, p-value = 1.007e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2930709 0.4732100
## sample estimates:
## cor
## 0.3868246
#相関表9列目
cor.test(InputData$health_mean_T1, InputData$health_mean_T2, method = "pearson") #healthT1-healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T1 and InputData$health_mean_T2
## t = 8.2219, df = 342, p-value = 4.213e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3139969 0.4909030
## sample estimates:
## cor
## 0.4062497
cor.test(InputData$health_mean_T1, InputData$positive_mean_T1, method = "pearson") #healthT1-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T1 and InputData$positive_mean_T1
## t = 9.2399, df = 410, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3318448 0.4920244
## sample estimates:
## cor
## 0.4151468
cor.test(InputData$health_mean_T1, InputData$negative_mean_T1, method = "pearson") #healthT1-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T1 and InputData$negative_mean_T1
## t = -9.0084, df = 410, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4840823 -0.3225354
## sample estimates:
## cor
## -0.4064809
cor.test(InputData$health_mean_T1, InputData$environment_mean_T2, method = "pearson") #healthT1-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T1 and InputData$environment_mean_T2
## t = 0.63315, df = 342, p-value = 0.5271
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07178439 0.13945324
## sample estimates:
## cor
## 0.03421657
cor.test(InputData$health_mean_T1, InputData$bis_mean_T2, method = "pearson") #healthT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T1 and InputData$bis_mean_T2
## t = -2.4788, df = 342, p-value = 0.01366
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23528543 -0.02749441
## sample estimates:
## cor
## -0.1328494
cor.test(InputData$health_mean_T1, InputData$bas_mean_T2, method = "pearson") #healthT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T1 and InputData$bas_mean_T2
## t = 3.0475, df = 342, p-value = 0.002487
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05784989 0.26380214
## sample estimates:
## cor
## 0.1625965
#相関表10列目
cor.test(InputData$health_mean_T2, InputData$positive_mean_T1, method = "pearson") #healthT2-positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T2 and InputData$positive_mean_T1
## t = 6.376, df = 342, p-value = 5.89e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2280661 0.4173051
## sample estimates:
## cor
## 0.3259468
cor.test(InputData$health_mean_T2, InputData$negative_mean_T1, method = "pearson") #healthT2-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T2 and InputData$negative_mean_T1
## t = -5.5936, df = 342, p-value = 4.559e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3835164 -0.1895785
## sample estimates:
## cor
## -0.289516
cor.test(InputData$health_mean_T2, InputData$environment_mean_T2, method = "pearson") #healthT2-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T2 and InputData$environment_mean_T2
## t = 6.3136, df = 342, p-value = 8.463e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2250390 0.4146664
## sample estimates:
## cor
## 0.3230919
cor.test(InputData$health_mean_T2, InputData$bis_mean_T2, method = "pearson") #healthT2-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T2 and InputData$bis_mean_T2
## t = -3.8763, df = 342, p-value = 0.0001271
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3042904 -0.1016131
## sample estimates:
## cor
## -0.2051501
cor.test(InputData$health_mean_T2, InputData$bas_mean_T2, method = "pearson") #healthT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$health_mean_T2 and InputData$bas_mean_T2
## t = 7.2821, df = 342, p-value = 2.28e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2711510 0.4545194
## sample estimates:
## cor
## 0.3663872
#相関表11列目
cor.test(InputData$positive_mean_T1, InputData$negative_mean_T1, method = "pearson") #positiveT1-negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$positive_mean_T1 and InputData$negative_mean_T1
## t = -0.79847, df = 410, p-value = 0.4251
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13549893 0.05742727
## sample estimates:
## cor
## -0.03940304
cor.test(InputData$positive_mean_T1, InputData$environment_mean_T2, method = "pearson") #positiveT1-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$positive_mean_T1 and InputData$environment_mean_T2
## t = 2.9931, df = 342, p-value = 0.002962
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05495661 0.26109955
## sample estimates:
## cor
## 0.1597694
cor.test(InputData$positive_mean_T1, InputData$bis_mean_T2, method = "pearson") #positiveT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$positive_mean_T1 and InputData$bis_mean_T2
## t = -2.0374, df = 342, p-value = 0.04238
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.212785501 -0.003811129
## sample estimates:
## cor
## -0.1095082
cor.test(InputData$positive_mean_T1, InputData$bas_mean_T2, method = "pearson") #positiveT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$positive_mean_T1 and InputData$bas_mean_T2
## t = 6.2745, df = 342, p-value = 1.061e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2231361 0.4130061
## sample estimates:
## cor
## 0.3212965
#相関表12列目
cor.test(InputData$negative_mean_T1, InputData$environment_mean_T2, method = "pearson") #negativeT1-envirionmentT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$negative_mean_T1 and InputData$environment_mean_T2
## t = -1.148, df = 342, p-value = 0.2518
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.16660705 0.04407263
## sample estimates:
## cor
## -0.06195734
cor.test(InputData$negative_mean_T1, InputData$bis_mean_T2, method = "pearson") #negativeT1-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$negative_mean_T1 and InputData$bis_mean_T2
## t = 4.4546, df = 342, p-value = 1.14e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1316978 0.3317054
## sample estimates:
## cor
## 0.2341779
cor.test(InputData$negative_mean_T1, InputData$bas_mean_T2, method = "pearson") #negativeT1-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$negative_mean_T1 and InputData$bas_mean_T2
## t = -0.020087, df = 342, p-value = 0.984
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1068152 0.1046671
## sample estimates:
## cor
## -0.001086193
#相関表13列目
cor.test(InputData$environment_mean_T2, InputData$bis_mean_T2, method = "pearson") #environmentT2-bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_T2 and InputData$bis_mean_T2
## t = -2.4459, df = 342, p-value = 0.01495
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23361857 -0.02573185
## sample estimates:
## cor
## -0.1311163
cor.test(InputData$environment_mean_T2, InputData$bas_mean_T2, method = "pearson") #environmentT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_T2 and InputData$bas_mean_T2
## t = 4.5889, df = 342, p-value = 6.264e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1386269 0.3379721
## sample estimates:
## cor
## 0.2408378
#相関表14列目
cor.test(InputData$bis_mean_T2, InputData$bas_mean_T2, method = "pearson") #bisT2-basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$bis_mean_T2 and InputData$bas_mean_T2
## t = 1.5444, df = 342, p-value = 0.1234
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02271904 0.18731497
## sample estimates:
## cor
## 0.08322211
library(GGally)
##
## Attaching package: 'GGally'
## The following object is masked from 'package:dplyr':
##
## nasa
cordata <- InputData %>% #散布図用のデータセットcordata作成
select(child_gender_T1, eoe_mean_T1:bas_mean_T2) %>% #性別とeoeからbasまでの列を抽出
select(-na.rm) #na.rmという謎変数が含まれていたので除外
names(cordata) #変数名確認
## [1] "child_gender_T1" "eoe_mean_T1" "eoe_mean_T2"
## [4] "lst_mean_T1" "lst_mean_T2" "aes_mean_T1"
## [7] "aes_mean_T2" "hsc_mean_T1" "hsc_mean_T2"
## [10] "health_mean_T1" "health_mean_T2" "positive_mean_T1"
## [13] "negative_mean_T1" "environment_mean_T2" "bis_mean_T2"
## [16] "bas_mean_T2"
# png("figure/corplot.png", width = 1200, height = 1200) #図の保存先指定
cor_plot <- ggpairs(cordata, mapping = aes(colour = factor(child_gender_T1), alpha=0.5)) #性別で色分けで作図
# dev.off() #保存
print(cor_plot) #出力
# 欠損値分析に使う変数を抽出
x <- InputData %>%
select_("eoe_mean_T1", "lst_mean_T1", "aes_mean_T1", "health_mean_T1", "positive_mean_T1", "negative_mean_T1", "eoe_mean_T2")
#全時点参加者と1時点目だけ参加者のカテゴリ化変数を作成
x <- x %>% mutate(particitation = if_else(eoe_mean_T2 >=1, "1", "0")) #全時点参加者を0に置換する(2時点目の変数に回答しているということは全時点参加者なので)
#ここでT1だけの参加者を1にカテゴリ化
x <- x %>% select_("eoe_mean_T1", "lst_mean_T1", "aes_mean_T1", "health_mean_T1", "positive_mean_T1", "negative_mean_T1", "particitation")
x$particitation[is.na(x$particitation)] <- 0 #particitation列のNAを0に置換
names(x) #変数名確認
## [1] "eoe_mean_T1" "lst_mean_T1" "aes_mean_T1"
## [4] "health_mean_T1" "positive_mean_T1" "negative_mean_T1"
## [7] "particitation"
x$particitation #念のため列が1,0で置換されたか確認
## [1] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1"
## [18] "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1"
## [35] "0" "1" "1" "0" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "1"
## [52] "1" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1"
## [69] "0" "1" "1" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "1" "1"
## [86] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0"
## [103] "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "1" "1" "1" "1" "0" "1" "1"
## [120] "0" "1" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1" "1" "1"
## [137] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1"
## [154] "0" "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "1" "1"
## [171] "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "0" "1" "1" "1" "1"
## [188] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1"
## [205] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1" "0"
## [222] "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1"
## [239] "1" "1" "1" "1" "0" "0" "1" "1" "1" "0" "1" "0" "1" "0" "1" "1" "1"
## [256] "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "1"
## [273] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1" "0"
## [290] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "0"
## [307] "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1"
## [324] "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1"
## [341] "1" "1" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "0"
## [358] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0"
## [375] "1" "1" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" "1" "1"
## [392] "1" "1" "1" "1" "1" "1" "1" "0" "0" "1" "1" "1" "1" "0" "1" "0" "0"
## [409] "1" "0" "0" "0"
x$particitation <- as.factor(x$particitation) #factor型に変換
class(x$particitation)
## [1] "factor"
head(x)
#particitationを独立変数としてMANOVA
result <- manova(cbind(eoe_mean_T1,lst_mean_T1,aes_mean_T1, health_mean_T1,positive_mean_T1, negative_mean_T1) ~ particitation, data = x)
summary(result) #出力
## Df Pillai approx F num Df den Df Pr(>F)
## particitation 1 0.010309 0.70314 6 405 0.6472
## Residuals 410
#結果メモ:従属変数間にparticipationの効果は見られなかった。
library(lavaan)
## This is lavaan 0.6-1
## lavaan is BETA software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
library(semPlot)
library(semTools)
##
## ###############################################################################
## This is semTools 0.5-0
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
##
## Attaching package: 'semTools'
## The following object is masked from 'package:psych':
##
## skew
手順としては、(1)因子不変性の比較と最適なモデルの選択、(2)最適なモデルの適合度や因子負荷量や信頼性係数の算出
#縦断的な測定不変性を検討するため、因子分析に用いるデータを【tidydata】型にする。
InputData_tidy <- InputData %>%
gather(key = "time", value = "hsc1", hsc1_T1, hsc1_T2) %>%
gather(key = "jikan1", value = "hsc2", hsc2_T1, hsc2_T2) %>%
gather(key = "jikan2", value = "hsc3", hsc3_T1, hsc3_T2) %>%
gather(key = "jikan3", value = "hsc4", hsc4_T1, hsc4_T2) %>%
gather(key = "jikan4", value = "hsc5", hsc5_T1, hsc5_T2) %>%
gather(key = "jikan5", value = "hsc6", hsc6_T1, hsc6_T2) %>%
gather(key = "jikan7", value = "hsc8", hsc8_T1, hsc8_T2) %>%
gather(key = "jikan8", value = "hsc9", hsc9_T1, hsc9_T2) %>%
gather(key = "jikan9", value = "hsc10", hsc10_T1, hsc10_T2) %>%
gather(key = "jikan10", value = "hsc11", hsc11_T1, hsc11_T2) %>%
gather(key = "jikan11", value = "hsc12", hsc12_T1, hsc12_T2)
InputData_tidy$time <- sub("hsc1_T", "", InputData_tidy$time) #time列のデータから"hsc_T1"文字を削除
InputData_tidy <- InputData_tidy %>% select(-starts_with("jikan")) #jikan列を削除
InputData_tidy$time <- as.numeric(InputData_tidy$time) #整数型に変換
names(InputData_tidy) #列名も確認
## [1] "ID" "gardian_gender_T1" "gardian_age_T1"
## [4] "prefecture_T1" "area_T1" "married_T1"
## [7] "familyincome_T1" "pinincome_T1" "job_T1"
## [10] "child_gender_T1" "hsc7_T1" "health1_T1"
## [13] "health2_T1" "health3_T1" "health4_T1"
## [16] "health5_T1" "panas1_T1" "panas2_T1"
## [19] "panas3_T1" "panas4_T1" "panas5_T1"
## [22] "panas6_T1" "panas7_T1" "panas8_T1"
## [25] "panas9_T1" "panas10_T1" "panas11_T1"
## [28] "panas12_T1" "panas13_T1" "panas14_T1"
## [31] "panas15_T1" "panas16_T1" "gardian_gender_T2"
## [34] "gardian_age_T2" "prefecture_T2" "area_T2"
## [37] "married_T2" "familyincome_T2" "pinincome_T2"
## [40] "job_T2" "child_gender_T2" "environment1_T2"
## [43] "environment2_T2" "environment3_T2" "environment4_T2"
## [46] "environment5_T2" "environment6_T2" "environment7_T2"
## [49] "environment8_T2" "environment9_T2" "environment10_T2"
## [52] "environment11_T2" "hsc7_T2" "health1_T2"
## [55] "health2_T2" "health3_T2" "health4_T2"
## [58] "health5_T2" "bis1r_T2" "bas1_T2"
## [61] "bas2_T2" "bas3_T2" "bas4_T2"
## [64] "bis2_T2" "bas5_T2" "bas6_T2"
## [67] "bas7_T2" "bis3_T2" "bas8_T2"
## [70] "bas9_T2" "bis4_T2" "bas10_T2"
## [73] "bis5_T2" "bas11_T2" "bas12_T2"
## [76] "bis6r_T2" "bas13_T2" "bis7_T2"
## [79] "bis1_T2" "bis6_T2" "eoe_mean_T1"
## [82] "na.rm" "eoe_mean_T2" "lst_mean_T1"
## [85] "lst_mean_T2" "aes_mean_T1" "aes_mean_T2"
## [88] "hsc_mean_T1" "hsc_mean_T2" "health_mean_T1"
## [91] "health_mean_T2" "positive_mean_T1" "negative_mean_T1"
## [94] "environment_mean_T2" "bis_mean_T2" "bas_mean_T2"
## [97] "time" "hsc1" "hsc2"
## [100] "hsc3" "hsc4" "hsc5"
## [103] "hsc6" "hsc8" "hsc9"
## [106] "hsc10" "hsc11" "hsc12"
model <-'
EOE =~ hsc4 + hsc6 + hsc8 + hsc9 + hsc12
LST =~ hsc2 + hsc11
AES =~ hsc1 + hsc3 + hsc5 + hsc10
'
measurementInvariance(model = model, data = InputData_tidy, std.lv = TRUE, strict = TRUE, fit.measures = c("cfi", "rmsea", "aic"), group= "time", missing = "fiml") #ものすごく推定時間かかる
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.residuals
## Model 5 : fit.means
##
## Chi Square Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 82 27636382 27637221 142144
## fit.loadings 90 27639691 27640436 145469 3324.4 8 <2e-16
## fit.intercepts 98 27640272 27640924 146066 597.1 8 <2e-16
## fit.residuals 109 27647115 27647639 152931 6865.6 11 <2e-16
## fit.means 112 27647114 27647603 152936 5.0 3 0.1722
##
## fit.configural
## fit.loadings ***
## fit.intercepts ***
## fit.residuals ***
## fit.means
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea aic cfi.delta rmsea.delta aic.delta
## fit.configural 0.894 0.064 27636382 NA NA NA
## fit.loadings 0.892 0.062 27639691 0.002 0.002 3308.355
## fit.intercepts 0.891 0.059 27640272 0.000 0.002 581.125
## fit.residuals 0.886 0.058 27647115 0.005 0.002 6843.596
## fit.means 0.886 0.057 27647114 0.000 0.001 1.005
config <- cfa(model, data = InputData_tidy, group = "time", missing = "fiml")
summary(config, fit.measures = TRUE, standardized = TRUE) #列が長いから結構推定時間長くかかる
## lavaan (0.6-1) converged normally after 117 iterations
##
## Number of observations per group
## 1 421888
## 2 421820
## Number of missing patterns per group
## 1 1024
## 2 1024
##
## Estimator ML
## Model Fit Test Statistic 142144.379
## Degrees of freedom 82
## P-value (Chi-square) 0.000
##
## Chi-square for each group:
##
## 1 77668.814
## 2 64475.564
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1342268.910
## Degrees of freedom 110
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.894
## Tucker-Lewis Index (TLI) 0.858
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -13818119.123
## Loglikelihood unrestricted model (H1) -13747046.934
##
## Number of free parameters 72
## Akaike (AIC) 27636382.246
## Bayesian (BIC) 27637220.726
## Sample-size adjusted Bayesian (BIC) 27636991.907
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064
## 90 Percent Confidence Interval 0.064 0.064
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Standard
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EOE =~
## hsc4 1.000 0.715 0.528
## hsc6 1.223 0.005 249.271 0.000 0.874 0.669
## hsc8 1.140 0.005 251.984 0.000 0.815 0.654
## hsc9 0.671 0.004 183.666 0.000 0.480 0.403
## hsc12 1.126 0.005 241.538 0.000 0.805 0.609
## LST =~
## hsc2 1.000 0.848 0.603
## hsc11 0.919 0.005 193.522 0.000 0.779 0.580
## AES =~
## hsc1 1.000 0.404 0.324
## hsc3 1.655 0.011 145.943 0.000 0.668 0.493
## hsc5 2.496 0.016 159.590 0.000 1.008 0.695
## hsc10 2.236 0.015 151.911 0.000 0.903 0.644
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EOE ~~
## LST 0.440 0.002 177.554 0.000 0.725 0.725
## AES 0.099 0.001 108.280 0.000 0.343 0.343
## LST ~~
## AES 0.081 0.001 72.783 0.000 0.236 0.236
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hsc4 4.191 0.002 1939.280 0.000 4.191 3.097
## .hsc6 4.808 0.002 2310.496 0.000 4.808 3.677
## .hsc8 4.441 0.002 2237.092 0.000 4.441 3.562
## .hsc9 4.135 0.002 2171.329 0.000 4.135 3.476
## .hsc12 4.584 0.002 2175.437 0.000 4.584 3.467
## .hsc2 4.093 0.002 1820.420 0.000 4.093 2.909
## .hsc11 4.616 0.002 2148.252 0.000 4.616 3.434
## .hsc1 4.243 0.002 2214.998 0.000 4.243 3.410
## .hsc3 4.698 0.002 2167.556 0.000 4.698 3.468
## .hsc5 5.225 0.002 2260.766 0.000 5.225 3.606
## .hsc10 5.590 0.002 2497.529 0.000 5.590 3.986
## EOE 0.000 0.000 0.000
## LST 0.000 0.000 0.000
## AES 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hsc4 1.321 0.003 379.102 0.000 1.321 0.721
## .hsc6 0.946 0.003 313.816 0.000 0.946 0.553
## .hsc8 0.891 0.003 326.221 0.000 0.891 0.573
## .hsc9 1.185 0.003 409.086 0.000 1.185 0.837
## .hsc12 1.100 0.003 346.993 0.000 1.100 0.630
## .hsc2 1.260 0.005 269.468 0.000 1.260 0.636
## .hsc11 1.199 0.004 290.004 0.000 1.199 0.664
## .hsc1 1.385 0.003 429.612 0.000 1.385 0.895
## .hsc3 1.389 0.004 367.009 0.000 1.389 0.757
## .hsc5 1.084 0.005 224.187 0.000 1.084 0.516
## .hsc10 1.152 0.004 269.118 0.000 1.152 0.586
## EOE 0.511 0.003 149.425 0.000 1.000 1.000
## LST 0.720 0.005 140.884 0.000 1.000 1.000
## AES 0.163 0.002 86.312 0.000 1.000 1.000
##
##
## Group 2 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EOE =~
## hsc4 1.000 0.714 0.528
## hsc6 1.225 0.005 249.485 0.000 0.875 0.669
## hsc8 1.142 0.005 252.176 0.000 0.816 0.654
## hsc9 0.671 0.004 183.604 0.000 0.479 0.403
## hsc12 1.124 0.005 241.525 0.000 0.803 0.607
## LST =~
## hsc2 1.000 0.849 0.604
## hsc11 0.916 0.005 192.540 0.000 0.778 0.579
## AES =~
## hsc1 1.000 0.216 0.203
## hsc3 3.159 0.034 93.355 0.000 0.681 0.503
## hsc5 4.586 0.048 95.364 0.000 0.989 0.683
## hsc10 4.224 0.046 91.761 0.000 0.911 0.650
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EOE ~~
## LST 0.440 0.002 177.482 0.000 0.725 0.725
## AES 0.053 0.001 80.084 0.000 0.344 0.344
## LST ~~
## AES 0.037 0.001 56.977 0.000 0.203 0.203
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hsc4 4.192 0.002 1939.504 0.000 4.192 3.097
## .hsc6 4.809 0.002 2310.847 0.000 4.809 3.678
## .hsc8 4.442 0.002 2237.419 0.000 4.442 3.562
## .hsc9 4.136 0.002 2171.451 0.000 4.136 3.477
## .hsc12 4.584 0.002 2175.709 0.000 4.584 3.468
## .hsc2 4.093 0.002 1820.474 0.000 4.093 2.909
## .hsc11 4.616 0.002 2148.272 0.000 4.616 3.434
## .hsc1 4.180 0.002 2336.452 0.000 4.180 3.930
## .hsc3 4.699 0.002 2167.811 0.000 4.699 3.469
## .hsc5 5.227 0.002 2259.724 0.000 5.227 3.608
## .hsc10 5.592 0.002 2496.739 0.000 5.592 3.988
## EOE 0.000 0.000 0.000
## LST 0.000 0.000 0.000
## AES 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hsc4 1.321 0.003 379.315 0.000 1.321 0.721
## .hsc6 0.944 0.003 313.674 0.000 0.944 0.552
## .hsc8 0.889 0.003 326.253 0.000 0.889 0.572
## .hsc9 1.185 0.003 409.231 0.000 1.185 0.838
## .hsc12 1.103 0.003 347.787 0.000 1.103 0.631
## .hsc2 1.258 0.005 267.996 0.000 1.258 0.636
## .hsc11 1.201 0.004 289.765 0.000 1.201 0.665
## .hsc1 1.085 0.003 410.472 0.000 1.085 0.959
## .hsc3 1.370 0.004 358.122 0.000 1.370 0.747
## .hsc5 1.120 0.005 226.285 0.000 1.120 0.534
## .hsc10 1.136 0.004 255.347 0.000 1.136 0.578
## EOE 0.510 0.003 149.423 0.000 1.000 1.000
## LST 0.721 0.005 140.585 0.000 1.000 1.000
## AES 0.047 0.001 48.676 0.000 1.000 1.000
cfa.config.plot <- semPaths(config, "std", edge.label.cex=.8, fade = FALSE, gray = TRUE, mar=c(6,1,3,1), style="lisrel") #作図
分析の手順としては、(1)因子数の決定、(2)それにもとづく回転と因子負荷量の推定、(3)信頼性係数の算出、(4)再度合計得点の算出と列追加
library(psych)
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
library(GPArotation)
#因子分析用データセットの作成
library(tidyverse)
environment.data <- InputData[, 53:63] #なぜかdplyr::selectがつかえない…
names(environment.data) #変数名確認
## [1] "environment1_T2" "environment2_T2" "environment3_T2"
## [4] "environment4_T2" "environment5_T2" "environment6_T2"
## [7] "environment7_T2" "environment8_T2" "environment9_T2"
## [10] "environment10_T2" "environment11_T2"
結果メモ:4因子解が推奨された
fa.parallel(environment.data, fm = "ml", fa = "fa") #fm =最尤法推定, fa=主因子法抽出
## Parallel analysis suggests that the number of factors = 5 and the number of components = NA
VSS(environment.data, n = 10) #n = 10因子までの数値を算出
##
## Very Simple Structure
## Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
## n.obs = n.obs, plot = plot, title = title, use = use, cor = cor)
## VSS complexity 1 achieves a maximimum of 0.81 with 2 factors
## VSS complexity 2 achieves a maximimum of 0.91 with 3 factors
##
## The Velicer MAP achieves a minimum of 0.06 with 1 factors
## BIC achieves a minimum of NA with 4 factors
## Sample Size adjusted BIC achieves a minimum of NA with 4 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC
## 1 0.77 0.00 0.056 44 9.2e+02 2.2e-164 6.62 0.77 0.222 656 795
## 2 0.81 0.87 0.065 34 4.7e+02 6.7e-79 3.81 0.87 0.179 269 377
## 3 0.80 0.91 0.060 25 2.1e+02 9.1e-32 1.97 0.93 0.137 63 142
## 4 0.63 0.88 0.066 17 2.1e+01 2.5e-01 1.41 0.95 0.024 -82 -28
## 5 0.62 0.86 0.101 10 6.7e+00 7.5e-01 1.22 0.96 0.000 -53 -22
## 6 0.63 0.85 0.141 4 8.8e-01 9.3e-01 1.23 0.96 0.000 -23 -11
## 7 0.59 0.82 0.203 -1 2.2e-02 NA 0.98 0.97 NA NA NA
## 8 0.63 0.85 0.318 -5 5.3e-05 NA 0.95 0.97 NA NA NA
## 9 0.63 0.85 0.493 -8 3.8e-08 NA 1.02 0.97 NA NA NA
## 10 0.63 0.85 1.000 -10 0.0e+00 NA 1.01 0.97 NA NA NA
## complex eChisq SRMR eCRMS eBIC
## 1 1.0 8.4e+02 1.4e-01 0.1524 577
## 2 1.2 3.4e+02 8.7e-02 0.1107 139
## 3 1.2 7.6e+01 4.1e-02 0.0608 -74
## 4 1.4 5.7e+00 1.1e-02 0.0202 -97
## 5 1.6 1.2e+00 5.2e-03 0.0121 -59
## 6 1.6 1.8e-01 2.0e-03 0.0074 -24
## 7 1.7 2.0e-03 2.1e-04 NA NA
## 8 1.7 5.1e-06 1.1e-05 NA NA
## 9 1.7 5.5e-09 3.5e-07 NA NA
## 10 1.7 1.1e-14 5.0e-10 NA NA
factor4 <- fa(environment.data, nfactors = 4, rotate = "promax", fm = "ml", scores = TRUE)
print(factor4, sort = TRUE)
## Factor Analysis using method = ml
## Call: fa(r = environment.data, nfactors = 4, rotate = "promax", scores = TRUE,
## fm = "ml")
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## item ML2 ML1 ML3 ML4 h2 u2 com
## environment2_T2 2 1.06 -0.06 -0.11 -0.09 0.87 0.126 1.0
## environment1_T2 1 0.97 -0.09 -0.10 -0.06 0.72 0.276 1.0
## environment6_T2 6 0.71 0.06 0.06 0.01 0.60 0.399 1.0
## environment5_T2 5 0.56 -0.04 0.12 0.11 0.49 0.515 1.2
## environment11_T2 11 0.39 0.16 0.08 0.06 0.31 0.691 1.5
## environment10_T2 10 -0.01 1.02 -0.03 -0.06 0.98 0.020 1.0
## environment9_T2 9 -0.04 0.83 -0.02 0.02 0.67 0.328 1.0
## environment4_T2 4 -0.08 -0.04 1.03 -0.03 0.92 0.078 1.0
## environment3_T2 3 0.23 -0.01 0.65 -0.02 0.66 0.336 1.3
## environment8_T2 8 0.06 -0.02 -0.10 0.91 0.78 0.216 1.0
## environment7_T2 7 -0.09 -0.01 0.05 0.79 0.60 0.396 1.0
##
## ML2 ML1 ML3 ML4
## SS loadings 3.02 1.69 1.49 1.41
## Proportion Var 0.27 0.15 0.14 0.13
## Cumulative Var 0.27 0.43 0.56 0.69
## Proportion Explained 0.40 0.22 0.20 0.18
## Cumulative Proportion 0.40 0.62 0.82 1.00
##
## With factor correlations of
## ML2 ML1 ML3 ML4
## ML2 1.00 0.37 0.71 0.43
## ML1 0.37 1.00 0.25 0.30
## ML3 0.71 0.25 1.00 0.44
## ML4 0.43 0.30 0.44 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 55 and the objective function was 6.05 with Chi Square of 2459.95
## The degrees of freedom for the model are 17 and the objective function was 0.05
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 344 with the empirical chi square 5.4 with prob < 1
## The total number of observations was 412 with Likelihood Chi Square = 19.22 with prob < 0.32
##
## Tucker Lewis Index of factoring reliability = 0.997
## RMSEA index = 0.019 and the 90 % confidence intervals are 0 0.05
## BIC = -83.14
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML2 ML1 ML3 ML4
## Correlation of (regression) scores with factors 0.97 0.99 0.97 0.92
## Multiple R square of scores with factors 0.94 0.98 0.94 0.85
## Minimum correlation of possible factor scores 0.88 0.96 0.88 0.70
factor1 <- fa(environment.data, nfactors = 1, rotate = "none", fm = "ml", scores = TRUE)
print(factor1, sort = TRUE) #結果出力
## Factor Analysis using method = ml
## Call: fa(r = environment.data, nfactors = 1, rotate = "none", scores = TRUE,
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## V ML1 h2 u2 com
## environment2_T2 2 0.87 0.759 0.24 1
## environment1_T2 1 0.81 0.659 0.34 1
## environment6_T2 6 0.79 0.628 0.37 1
## environment3_T2 3 0.72 0.512 0.49 1
## environment5_T2 5 0.71 0.498 0.50 1
## environment4_T2 4 0.67 0.447 0.55 1
## environment11_T2 11 0.54 0.296 0.70 1
## environment8_T2 8 0.38 0.144 0.86 1
## environment7_T2 7 0.31 0.098 0.90 1
## environment10_T2 10 0.31 0.096 0.90 1
## environment9_T2 9 0.26 0.069 0.93 1
##
## ML1
## SS loadings 4.21
## Proportion Var 0.38
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 55 and the objective function was 6.05 with Chi Square of 2459.95
## The degrees of freedom for the model are 44 and the objective function was 2.22
##
## The root mean square of the residuals (RMSR) is 0.14
## The df corrected root mean square of the residuals is 0.16
##
## The harmonic number of observations is 344 with the empirical chi square 749.35 with prob < 4.4e-129
## The total number of observations was 412 with Likelihood Chi Square = 902.78 with prob < 1.1e-160
##
## Tucker Lewis Index of factoring reliability = 0.553
## RMSEA index = 0.219 and the 90 % confidence intervals are 0.206 0.23
## BIC = 637.85
## Fit based upon off diagonal values = 0.88
## Measures of factor score adequacy
## ML1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.91
## Minimum correlation of possible factor scores 0.83
#項目7,9,10削除データ作成
environment.data.2 <- environment.data %>% select_("environment1_T2","environment2_T2", "environment3_T2", "environment4_T2", "environment5_T2", "environment6_T2", "environment8_T2", "environment11_T2")
names(environment.data.2) #変数名確認
## [1] "environment1_T2" "environment2_T2" "environment3_T2"
## [4] "environment4_T2" "environment5_T2" "environment6_T2"
## [7] "environment8_T2" "environment11_T2"
#再度因子分析
factor1.second <- fa(environment.data.2, nfactors = 1, rotate = "none", fm = "ml", scores = TRUE)
print(factor1.second, sort = TRUE) #結果出力
## Factor Analysis using method = ml
## Call: fa(r = environment.data.2, nfactors = 1, rotate = "none", scores = TRUE,
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## V ML1 h2 u2 com
## environment2_T2 2 0.89 0.78 0.22 1
## environment1_T2 1 0.83 0.68 0.32 1
## environment6_T2 6 0.79 0.62 0.38 1
## environment3_T2 3 0.71 0.51 0.49 1
## environment5_T2 5 0.70 0.49 0.51 1
## environment4_T2 4 0.66 0.44 0.56 1
## environment11_T2 8 0.53 0.28 0.72 1
## environment8_T2 7 0.35 0.12 0.88 1
##
## ML1
## SS loadings 3.92
## Proportion Var 0.49
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 28 and the objective function was 4.15 with Chi Square of 1692.9
## The degrees of freedom for the model are 20 and the objective function was 0.56
##
## The root mean square of the residuals (RMSR) is 0.07
## The df corrected root mean square of the residuals is 0.08
##
## The harmonic number of observations is 344 with the empirical chi square 89.27 with prob < 1e-10
## The total number of observations was 412 with Likelihood Chi Square = 226.85 with prob < 5.1e-37
##
## Tucker Lewis Index of factoring reliability = 0.826
## RMSEA index = 0.16 and the 90 % confidence intervals are 0.14 0.178
## BIC = 106.43
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## ML1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.91
## Minimum correlation of possible factor scores 0.83
omega(environment.data.2,3, fm = "ml") #算出
## Omega
## Call: omega(m = environment.data.2, nfactors = 3, fm = "ml")
## Alpha: 0.88
## G.6: 0.89
## Omega Hierarchical: 0.77
## Omega H asymptotic: 0.85
## Omega Total 0.91
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## environment1_T2 0.75 0.41 0.73 0.27 0.76
## environment2_T2 0.83 0.44 0.88 0.12 0.78
## environment3_T2 0.70 0.71 1.00 0.00 0.49
## environment4_T2 0.63 0.45 0.62 0.38 0.64
## environment5_T2 0.69 0.38 0.62 0.38 0.76
## environment6_T2 0.73 0.22 0.62 0.38 0.86
## environment8_T2 0.35 0.27 0.20 0.80 0.59
## environment11_T2 0.49 0.28 0.72 0.86
##
## With eigenvalues of:
## g F1* F2* F3*
## 3.48 0.44 0.73 0.29
##
## general/max 4.8 max/min = 2.53
## mean percent general = 0.72 with sd = 0.13 and cv of 0.18
## Explained Common Variance of the general factor = 0.71
##
## The degrees of freedom are 7 and the fit is 0.01
## The number of observations was 412 with Chi Square = 5.83 with prob < 0.56
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.054
## BIC = -36.32
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.65
## The number of observations was 412 with Chi Square = 264.25 with prob < 1.5e-44
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.1
##
## RMSEA index = 0.173 and the 10 % confidence intervals are 0.154 0.191
## BIC = 143.83
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.90 0.60 0.90 0.58
## Multiple R square of scores with factors 0.81 0.36 0.81 0.33
## Minimum correlation of factor score estimates 0.62 -0.29 0.61 -0.34
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.91 0.85 0.89 0.56
## Omega general for total scores and subscales 0.77 0.72 0.50 0.40
## Omega group for total scores and subscales 0.09 0.13 0.38 0.16
InputData <- InputData %>%
dplyr::mutate(environment_mean_afterEFA_T2 = (environment1_T2 + environment2_T2 + environment3_T2 + environment4_T2 + environment5_T2 + environment6_T2 + environment8_T2 + environment11_T2)/8, na.rm = TRUE)
names(InputData) #合計得点追加されたか確認
## [1] "ID" "gardian_gender_T1"
## [3] "gardian_age_T1" "prefecture_T1"
## [5] "area_T1" "married_T1"
## [7] "familyincome_T1" "pinincome_T1"
## [9] "job_T1" "child_gender_T1"
## [11] "hsc1_T1" "hsc2_T1"
## [13] "hsc3_T1" "hsc4_T1"
## [15] "hsc5_T1" "hsc6_T1"
## [17] "hsc7_T1" "hsc8_T1"
## [19] "hsc9_T1" "hsc10_T1"
## [21] "hsc11_T1" "hsc12_T1"
## [23] "health1_T1" "health2_T1"
## [25] "health3_T1" "health4_T1"
## [27] "health5_T1" "panas1_T1"
## [29] "panas2_T1" "panas3_T1"
## [31] "panas4_T1" "panas5_T1"
## [33] "panas6_T1" "panas7_T1"
## [35] "panas8_T1" "panas9_T1"
## [37] "panas10_T1" "panas11_T1"
## [39] "panas12_T1" "panas13_T1"
## [41] "panas14_T1" "panas15_T1"
## [43] "panas16_T1" "gardian_gender_T2"
## [45] "gardian_age_T2" "prefecture_T2"
## [47] "area_T2" "married_T2"
## [49] "familyincome_T2" "pinincome_T2"
## [51] "job_T2" "child_gender_T2"
## [53] "environment1_T2" "environment2_T2"
## [55] "environment3_T2" "environment4_T2"
## [57] "environment5_T2" "environment6_T2"
## [59] "environment7_T2" "environment8_T2"
## [61] "environment9_T2" "environment10_T2"
## [63] "environment11_T2" "hsc1_T2"
## [65] "hsc2_T2" "hsc3_T2"
## [67] "hsc4_T2" "hsc5_T2"
## [69] "hsc6_T2" "hsc7_T2"
## [71] "hsc8_T2" "hsc9_T2"
## [73] "hsc10_T2" "hsc11_T2"
## [75] "hsc12_T2" "health1_T2"
## [77] "health2_T2" "health3_T2"
## [79] "health4_T2" "health5_T2"
## [81] "bis1r_T2" "bas1_T2"
## [83] "bas2_T2" "bas3_T2"
## [85] "bas4_T2" "bis2_T2"
## [87] "bas5_T2" "bas6_T2"
## [89] "bas7_T2" "bis3_T2"
## [91] "bas8_T2" "bas9_T2"
## [93] "bis4_T2" "bas10_T2"
## [95] "bis5_T2" "bas11_T2"
## [97] "bas12_T2" "bis6r_T2"
## [99] "bas13_T2" "bis7_T2"
## [101] "bis1_T2" "bis6_T2"
## [103] "eoe_mean_T1" "na.rm"
## [105] "eoe_mean_T2" "lst_mean_T1"
## [107] "lst_mean_T2" "aes_mean_T1"
## [109] "aes_mean_T2" "hsc_mean_T1"
## [111] "hsc_mean_T2" "health_mean_T1"
## [113] "health_mean_T2" "positive_mean_T1"
## [115] "negative_mean_T1" "environment_mean_T2"
## [117] "bis_mean_T2" "bas_mean_T2"
## [119] "environment_mean_afterEFA_T2"
environment_mean_afterEFA_T2_count <- dplyr::count(InputData, environment_mean_afterEFA_T2)
knitr::kable(environment_mean_afterEFA_T2_count, digits = 2) #テーブル化
| environment_mean_afterEFA_T2 | n |
|---|---|
| 1.00 | 1 |
| 1.38 | 1 |
| 2.62 | 3 |
| 2.75 | 1 |
| 3.00 | 3 |
| 3.12 | 1 |
| 3.25 | 9 |
| 3.38 | 4 |
| 3.50 | 6 |
| 3.62 | 11 |
| 3.75 | 13 |
| 3.88 | 21 |
| 4.00 | 36 |
| 4.12 | 26 |
| 4.25 | 27 |
| 4.38 | 19 |
| 4.50 | 9 |
| 4.62 | 24 |
| 4.75 | 20 |
| 4.88 | 14 |
| 5.00 | 10 |
| 5.12 | 14 |
| 5.25 | 7 |
| 5.38 | 8 |
| 5.50 | 9 |
| 5.62 | 7 |
| 5.75 | 7 |
| 5.88 | 5 |
| 6.00 | 5 |
| 6.12 | 7 |
| 6.25 | 6 |
| 6.38 | 3 |
| 6.50 | 2 |
| 6.62 | 1 |
| 6.75 | 1 |
| 7.00 | 3 |
| NA | 68 |
ggplot(data = InputData, mapping = aes(x = environment_mean_afterEFA_T2, fill = factor(environment_mean_afterEFA_T2))) +
geom_histogram(binwidth = 0.2) +
guides(fill = "none") #視覚化
environment_mean_afterEFA_T2_discriptive <-
InputData %>%
select_("environment_mean_afterEFA_T2") %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
environment.mean = mean (environment_mean_afterEFA_T2), #environment_mean_afterEFA_T2の平均
environment.sd = sd (environment_mean_afterEFA_T2)) #environment_mean_afterEFA_T2のSD
environment_mean_afterEFA_T2_discriptive #出力
cor.test(InputData$environment_mean_afterEFA_T2, InputData$hsc_mean_T1, method = "pearson") #hscT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$hsc_mean_T1
## t = 0.65783, df = 342, p-value = 0.5111
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07045709 0.14076110
## sample estimates:
## cor
## 0.03554899
cor.test(InputData$environment_mean_afterEFA_T2, InputData$hsc_mean_T2, method = "pearson") #hscT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$hsc_mean_T2
## t = 1.3102, df = 342, p-value = 0.191
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03533566 0.17510237
## sample estimates:
## cor
## 0.07066962
cor.test(InputData$environment_mean_afterEFA_T2, InputData$eoe_mean_T1, method = "pearson") #eoeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$eoe_mean_T1
## t = -1.0814, df = 342, p-value = 0.2803
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.16310927 0.04766057
## sample estimates:
## cor
## -0.05837485
cor.test(InputData$environment_mean_afterEFA_T2, InputData$eoe_mean_T2, method = "pearson") #eoeT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$eoe_mean_T2
## t = -1.2428, df = 342, p-value = 0.2148
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.17157569 0.03896656
## sample estimates:
## cor
## -0.06705094
cor.test(InputData$environment_mean_afterEFA_T2, InputData$lst_mean_T1, method = "pearson") #lstT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$lst_mean_T1
## t = 0.0026645, df = 342, p-value = 0.9979
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1055988 0.1058837
## sample estimates:
## cor
## 0.0001440796
cor.test(InputData$environment_mean_afterEFA_T2, InputData$lst_mean_T2, method = "pearson") #lstT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$lst_mean_T2
## t = -0.59664, df = 342, p-value = 0.5511
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13751797 0.07374705
## sample estimates:
## cor
## -0.03224564
cor.test(InputData$environment_mean_afterEFA_T2, InputData$aes_mean_T1, method = "pearson") #aesT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$aes_mean_T1
## t = 2.5117, df = 342, p-value = 0.01247
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02925753 0.23695160
## sample estimates:
## cor
## 0.1345824
cor.test(InputData$environment_mean_afterEFA_T2, InputData$aes_mean_T2, method = "pearson") #aesT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$aes_mean_T2
## t = 5.3356, df = 342, p-value = 1.737e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1766461 0.3720451
## sample estimates:
## cor
## 0.2772094
cor.test(InputData$environment_mean_afterEFA_T2, InputData$health_mean_T1, method = "pearson") #healthT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$health_mean_T1
## t = 0.264, df = 342, p-value = 0.7919
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09160556 0.11983429
## sample estimates:
## cor
## 0.01427393
cor.test(InputData$environment_mean_afterEFA_T2, InputData$health_mean_T2, method = "pearson") #healthT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$health_mean_T2
## t = 6.1096, df = 342, p-value = 2.718e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2150864 0.4059683
## sample estimates:
## cor
## 0.3136932
cor.test(InputData$environment_mean_afterEFA_T2, InputData$positive_mean_T1, method = "pearson") #positiveT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$positive_mean_T1
## t = 2.6607, df = 342, p-value = 0.008166
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03722533 0.24446603
## sample estimates:
## cor
## 0.142406
cor.test(InputData$environment_mean_afterEFA_T2, InputData$negative_mean_T1, method = "pearson") #negativeT1の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$negative_mean_T1
## t = -1.1513, df = 342, p-value = 0.2504
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.16678168 0.04389336
## sample estimates:
## cor
## -0.06213627
cor.test(InputData$environment_mean_afterEFA_T2, InputData$bis_mean_T2, method = "pearson") #bisT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$bis_mean_T2
## t = -2.1718, df = 342, p-value = 0.03056
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.21966912 -0.01103197
## sample estimates:
## cor
## -0.1166371
cor.test(InputData$environment_mean_afterEFA_T2, InputData$bas_mean_T2, method = "pearson") #basT2の相関
##
## Pearson's product-moment correlation
##
## data: InputData$environment_mean_afterEFA_T2 and InputData$bas_mean_T2
## t = 5.3045, df = 342, p-value = 2.034e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1750790 0.3706509
## sample estimates:
## cor
## 0.2757159
#モデル記述
lcs.health <- '
T1 =~ 1*health1_T1 + m*health2_T1 + n*health3_T1 + o*health4_T1 + p*health5_T1
T2 =~ 1*health1_T2 + m*health2_T2 + n*health3_T2 + o*health4_T2 + p*health5_T2
#誤差
health1_T1 ~~ health1_T1
health2_T1 ~~ health2_T1
health3_T1 ~~ health3_T1
health4_T1 ~~ health4_T1
health5_T1 ~~ health5_T1
health1_T2 ~~ health1_T2
health2_T2 ~~ health2_T2
health3_T2 ~~ health3_T2
health4_T2 ~~ health4_T2
health5_T2 ~~ health5_T2
#2時点間の同一項目間の誤差共分散を定義
health1_T1 ~~ health1_T2 #項目1
health2_T1 ~~ health2_T2 #項目2
health3_T1 ~~ health3_T2 #項目3
health4_T1 ~~ health4_T2 #項目4
health5_T1 ~~ health5_T2 #項目5
#free latent variances and covariances
LC ~~ var.LC*LC
T1 ~~ var.T1*T1
T1 ~~ eta*LC
#define latent difference score (fix loading to 1)
LC =~ 1*T2
#fix latent regression to 1 to define latent difference score
T2 ~ 1*T1
#fix FT2 variance to zero to define latent difference score
T2 ~~ 0*T2
#no correlations between FT2 and other latent variables
LC + T1 ~~ 0*T2
#means
LC ~ beta*1
T1 ~ alpha*1
'
result.lcs <- lavaan(lcs.health, data = InputData, fixed.x = FALSE, missing = "fiml", meanstructure = TRUE)
summary(result.lcs, fit.measures = TRUE) #結果出力
## lavaan (0.6-1) converged normally after 43 iterations
##
## Number of observations 412
## Number of missing patterns 2
##
## Estimator ML
## Model Fit Test Statistic 191.518
## Degrees of freedom 41
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2296.272
## Degrees of freedom 45
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.933
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4851.537
## Loglikelihood unrestricted model (H1) -4755.778
##
## Number of free parameters 24
## Akaike (AIC) 9751.074
## Bayesian (BIC) 9847.578
## Sample-size adjusted Bayesian (BIC) 9771.421
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.094
## 90 Percent Confidence Interval 0.081 0.108
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.059
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## T1 =~
## health1_T1 1.000
## health2_T1 (m) 0.974 0.007 131.369 0.000
## health3_T1 (n) 0.961 0.009 110.217 0.000
## health4_T1 (o) 0.926 0.010 89.564 0.000
## health5_T1 (p) 0.931 0.010 96.719 0.000
## T2 =~
## health1_T2 1.000
## health2_T2 (m) 0.974 0.007 131.369 0.000
## health3_T2 (n) 0.961 0.009 110.217 0.000
## health4_T2 (o) 0.926 0.010 89.564 0.000
## health5_T2 (p) 0.931 0.010 96.719 0.000
## LC =~
## T2 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## T2 ~
## T1 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .health1_T1 ~~
## .hlth1_T2 0.000 0.022 0.020 0.984
## .health2_T1 ~~
## .hlth2_T2 0.049 0.024 2.004 0.045
## .health3_T1 ~~
## .hlth3_T2 0.112 0.035 3.203 0.001
## .health4_T1 ~~
## .hlth4_T2 0.153 0.052 2.945 0.003
## .health5_T1 ~~
## .hlth5_T2 0.177 0.042 4.266 0.000
## T1 ~~
## LC (eta) -0.578 0.069 -8.321 0.000
## .T2 ~~
## LC 0.000
## T1 ~~
## .T2 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## LC (beta) 0.412 0.057 7.255 0.000
## T1 (alph) 3.640 0.054 67.887 0.000
## .hlt1_T1 0.000
## .hlt2_T1 0.000
## .hlt3_T1 0.000
## .hlt4_T1 0.000
## .hlt5_T1 0.000
## .hlt1_T2 0.000
## .hlt2_T2 0.000
## .hlt3_T2 0.000
## .hlt4_T2 0.000
## .hlt5_T2 0.000
## .T2 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hlt1_T1 0.361 0.036 10.023 0.000
## .hlt2_T1 0.382 0.037 10.328 0.000
## .hlt3_T1 0.665 0.055 12.089 0.000
## .hlt4_T1 0.793 0.063 12.637 0.000
## .hlt5_T1 0.697 0.057 12.255 0.000
## .hlt1_T2 0.228 0.026 8.669 0.000
## .hlt2_T2 0.273 0.028 9.630 0.000
## .hlt3_T2 0.439 0.040 10.946 0.000
## .hlt4_T2 0.893 0.074 12.055 0.000
## .hlt5_T2 0.595 0.052 11.383 0.000
## LC (v.LC) 1.006 0.091 11.109 0.000
## T1 (v.T1) 0.984 0.077 12.702 0.000
## .T2 0.000
#作図
lcs.plot <- semPaths(result.lcs, "std", edge.label.cex=.8, fade = FALSE, gray = TRUE, mar=c(6,1,3,1), style="mx")
change_score <- predict(result.lcs)
change_score <- as.data.frame(change_score) #データフレーム型に変換
health_change_score <- change_score %>% select_("LC") #差得点列だけを抽出
head(health_change_score) #個々人の潜在得点を確認(LC列が差得点)
#潜在差得点の列をInputDataの列に追加
InputData <- bind_cols(InputData, health_change_score)
names(InputData) #列追加されたか確認
## [1] "ID" "gardian_gender_T1"
## [3] "gardian_age_T1" "prefecture_T1"
## [5] "area_T1" "married_T1"
## [7] "familyincome_T1" "pinincome_T1"
## [9] "job_T1" "child_gender_T1"
## [11] "hsc1_T1" "hsc2_T1"
## [13] "hsc3_T1" "hsc4_T1"
## [15] "hsc5_T1" "hsc6_T1"
## [17] "hsc7_T1" "hsc8_T1"
## [19] "hsc9_T1" "hsc10_T1"
## [21] "hsc11_T1" "hsc12_T1"
## [23] "health1_T1" "health2_T1"
## [25] "health3_T1" "health4_T1"
## [27] "health5_T1" "panas1_T1"
## [29] "panas2_T1" "panas3_T1"
## [31] "panas4_T1" "panas5_T1"
## [33] "panas6_T1" "panas7_T1"
## [35] "panas8_T1" "panas9_T1"
## [37] "panas10_T1" "panas11_T1"
## [39] "panas12_T1" "panas13_T1"
## [41] "panas14_T1" "panas15_T1"
## [43] "panas16_T1" "gardian_gender_T2"
## [45] "gardian_age_T2" "prefecture_T2"
## [47] "area_T2" "married_T2"
## [49] "familyincome_T2" "pinincome_T2"
## [51] "job_T2" "child_gender_T2"
## [53] "environment1_T2" "environment2_T2"
## [55] "environment3_T2" "environment4_T2"
## [57] "environment5_T2" "environment6_T2"
## [59] "environment7_T2" "environment8_T2"
## [61] "environment9_T2" "environment10_T2"
## [63] "environment11_T2" "hsc1_T2"
## [65] "hsc2_T2" "hsc3_T2"
## [67] "hsc4_T2" "hsc5_T2"
## [69] "hsc6_T2" "hsc7_T2"
## [71] "hsc8_T2" "hsc9_T2"
## [73] "hsc10_T2" "hsc11_T2"
## [75] "hsc12_T2" "health1_T2"
## [77] "health2_T2" "health3_T2"
## [79] "health4_T2" "health5_T2"
## [81] "bis1r_T2" "bas1_T2"
## [83] "bas2_T2" "bas3_T2"
## [85] "bas4_T2" "bis2_T2"
## [87] "bas5_T2" "bas6_T2"
## [89] "bas7_T2" "bis3_T2"
## [91] "bas8_T2" "bas9_T2"
## [93] "bis4_T2" "bas10_T2"
## [95] "bis5_T2" "bas11_T2"
## [97] "bas12_T2" "bis6r_T2"
## [99] "bas13_T2" "bis7_T2"
## [101] "bis1_T2" "bis6_T2"
## [103] "eoe_mean_T1" "na.rm"
## [105] "eoe_mean_T2" "lst_mean_T1"
## [107] "lst_mean_T2" "aes_mean_T1"
## [109] "aes_mean_T2" "hsc_mean_T1"
## [111] "hsc_mean_T2" "health_mean_T1"
## [113] "health_mean_T2" "positive_mean_T1"
## [115] "negative_mean_T1" "environment_mean_T2"
## [117] "bis_mean_T2" "bas_mean_T2"
## [119] "environment_mean_afterEFA_T2" "LC"
health_change_score_discriptive <-
InputData %>%
dplyr::summarise(n = n (), #グループの人数を出力
health.lcs.mean = mean (LC), #差得点の平均
health1.lcs.sd = sd (LC)) #差得点のSD
health_change_score_discriptive #出力(平均はモデル推定値の切片と同じになっている)
#png("figure/healthchange_histogram.png", width = 600, height = 400)
health_change_score_histogram <- ggplot(data = InputData, mapping = aes(x = LC, colour = "black")) +
geom_histogram(binwidth = 0.2, colour="black") + guides(fill = "none") #視覚化
health_change_score_histogram + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.title = element_text(size = 16),
axis.text = element_text(size = 16)) +labs(x = "精神的健康の潜在差得点", y = "度数")
#dev.off()
shapiro.test(InputData$LC)
##
## Shapiro-Wilk normality test
##
## data: InputData$LC
## W = 0.9867, p-value = 0.0008022
#結果メモ:p < .05で正規分布ではないと判断。
#結果メモ:正規分布ではないという結果となった。
#HSCのT1の下位尺度得点のみのデータセットを作成
HSC.data <- InputData %>% select_("eoe_mean_T1", "lst_mean_T1", "aes_mean_T1")
head(HSC.data) #先頭行確認
library(cluster)
cluster.number <- clusGap(HSC.data, kmeans, K.max = 10, B = 2000, verbose = interactive())
cluster.number #結果出力 #クラスタ数は1が適切らしい。2~7だと4が適切。
## Clustering Gap statistic ["clusGap"] from call:
## clusGap(x = HSC.data, FUNcluster = kmeans, K.max = 10, B = 2000, verbose = interactive())
## B=2000 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
## --> Number of clusters (method 'firstSEmax', SE.factor=1): 1
## logW E.logW gap SE.sim
## [1,] 5.481052 6.216275 0.7352232 0.01435799
## [2,] 5.258224 5.928180 0.6699561 0.01276790
## [3,] 5.128158 5.832116 0.7039588 0.01432033
## [4,] 5.048883 5.742403 0.6935198 0.01571735
## [5,] 4.980838 5.663496 0.6826579 0.01531663
## [6,] 4.914262 5.590832 0.6765702 0.01555449
## [7,] 4.858445 5.527313 0.6688684 0.01566573
## [8,] 4.828178 5.470015 0.6418364 0.01506922
## [9,] 4.796747 5.417310 0.6205636 0.01554194
## [10,] 4.761201 5.371027 0.6098261 0.01556737
plot(cluster.number) #Gap統計量の視覚化
#4クラスタで分析
c4 <- kmeans(HSC.data, 4, algorithm = "Hartigan-Wong")
c4 #クラスタリング結果の表示
## K-means clustering with 4 clusters of sizes 67, 99, 94, 152
##
## Cluster means:
## eoe_mean_T1 lst_mean_T1 aes_mean_T1
## 1 3.495522 3.014925 5.544776
## 2 5.224242 5.924242 5.578283
## 3 3.823404 3.840426 3.550532
## 4 4.669737 4.315789 5.113487
##
## Clustering vector:
## [1] 4 3 4 3 4 4 1 2 3 3 3 2 4 2 4 4 3 4 4 1 2 3 3 2 3 4 3 4 2 2 3 4 3 1 3
## [36] 3 4 1 3 4 4 4 4 3 4 2 1 2 3 4 2 4 2 4 4 3 4 4 1 1 3 2 2 3 2 1 1 3 1 3
## [71] 3 1 4 4 4 4 2 4 3 3 3 3 1 3 4 3 3 4 2 3 3 2 1 3 4 4 3 2 4 2 4 2 1 1 4
## [106] 2 4 2 2 3 2 1 2 1 3 2 3 1 1 3 2 3 2 2 2 3 2 4 3 3 3 4 4 2 4 3 1 1 4 4
## [141] 4 4 3 1 2 3 1 4 2 1 4 4 2 4 4 3 4 3 4 3 4 2 1 2 4 4 4 1 2 3 2 3 1 2 2
## [176] 4 4 4 4 3 4 1 2 3 3 2 2 2 3 4 4 1 2 2 1 1 3 4 4 2 1 4 1 4 1 4 4 4 4 2
## [211] 4 4 3 3 4 1 3 3 4 4 4 2 4 4 2 4 1 4 3 4 3 4 1 4 4 2 2 3 4 2 2 1 1 2 4
## [246] 1 3 4 1 3 3 4 2 4 3 4 3 4 1 2 3 3 2 4 4 4 3 3 1 4 2 4 1 4 1 3 4 2 1 2
## [281] 4 2 4 4 1 2 4 4 4 2 3 1 1 2 4 3 4 3 4 1 4 2 3 3 4 4 2 2 4 4 2 4 2 2 1
## [316] 4 4 2 2 4 3 1 4 4 4 4 4 1 4 4 2 2 2 4 2 4 2 2 4 4 3 4 1 4 1 4 3 4 2 3
## [351] 1 3 3 2 1 2 4 1 3 4 2 2 4 3 4 4 1 4 4 3 3 2 1 4 4 4 2 3 2 2 4 4 1 1 4
## [386] 4 2 3 2 3 4 1 3 4 4 4 1 2 1 2 3 2 4 2 1 2 2 2 3 1 2 4
##
## Within cluster sum of squares by cluster:
## [1] 114.7694 147.9844 152.0924 182.0677
## (between_SS / total_SS = 57.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
library(tidyLPA)
## tidyLPA provides the functionality to carry out Latent Profile Analysis. Note that tidyLPA is still at the beta stage!
## Please report any bugs at https://github.com/jrosen48/tidyLPA or send an email to tidylpa@googlegroups.com.
compare_solutions(HSC.data, eoe_mean_T1, lst_mean_T1, aes_mean_T1)
#プロファイル数1
hsc.profile1 <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 1,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 1 profiles.
## LogLik is 1818.697
## BIC is 3673.52
## Entropy is 1
#プロファイル数2
hsc.profile2 <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 2,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 2 profiles.
## LogLik is 1772.653
## BIC is 3623.579
## Entropy is 0.825
#プロファイル数3
hsc.profile4 <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 3,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 3 profiles.
## LogLik is 1700.307
## BIC is 3521.035
## Entropy is 0.835
#プロファイル数4
hsc.profile4 <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 4,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 4 profiles.
## LogLik is 1691.142
## BIC is 3544.851
## Entropy is 0.79
#プロファイル数5
hsc.profile5 <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 5,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 5 profiles.
## LogLik is 1676.961
## BIC is 3558.637
## Entropy is 0.784
#プロファイル数6
hsc.profile6 <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 6,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 6 profiles.
## LogLik is 1673.968
## BIC is 3594.797
## Entropy is 0.778
hsc.profile <- estimate_profiles(HSC.data,
eoe_mean_T1, lst_mean_T1, aes_mean_T1,
model = 3,
n_profiles = 3,
return_orig_df = TRUE)
## Fit varying means and variances, covariances fixed to 0 (Model 3) model with 3 profiles.
## LogLik is 1700.307
## BIC is 3521.035
## Entropy is 0.835
plot_profiles(hsc.profile) #作図(素点)
png("figure/hsc_profile_centered_score.png", width = 600, height = 400)
plot_profiles(hsc.profile, to_center = TRUE, to_scale = TRUE) #作図(標準化得点)
dev.off()
## png
## 2
hsc.profile <- select_(hsc.profile, "profile")
names(hsc.profile)
## [1] "profile"
InputData <- bind_cols(InputData, hsc.profile)
names(InputData) #列追加されたか確認
## [1] "ID" "gardian_gender_T1"
## [3] "gardian_age_T1" "prefecture_T1"
## [5] "area_T1" "married_T1"
## [7] "familyincome_T1" "pinincome_T1"
## [9] "job_T1" "child_gender_T1"
## [11] "hsc1_T1" "hsc2_T1"
## [13] "hsc3_T1" "hsc4_T1"
## [15] "hsc5_T1" "hsc6_T1"
## [17] "hsc7_T1" "hsc8_T1"
## [19] "hsc9_T1" "hsc10_T1"
## [21] "hsc11_T1" "hsc12_T1"
## [23] "health1_T1" "health2_T1"
## [25] "health3_T1" "health4_T1"
## [27] "health5_T1" "panas1_T1"
## [29] "panas2_T1" "panas3_T1"
## [31] "panas4_T1" "panas5_T1"
## [33] "panas6_T1" "panas7_T1"
## [35] "panas8_T1" "panas9_T1"
## [37] "panas10_T1" "panas11_T1"
## [39] "panas12_T1" "panas13_T1"
## [41] "panas14_T1" "panas15_T1"
## [43] "panas16_T1" "gardian_gender_T2"
## [45] "gardian_age_T2" "prefecture_T2"
## [47] "area_T2" "married_T2"
## [49] "familyincome_T2" "pinincome_T2"
## [51] "job_T2" "child_gender_T2"
## [53] "environment1_T2" "environment2_T2"
## [55] "environment3_T2" "environment4_T2"
## [57] "environment5_T2" "environment6_T2"
## [59] "environment7_T2" "environment8_T2"
## [61] "environment9_T2" "environment10_T2"
## [63] "environment11_T2" "hsc1_T2"
## [65] "hsc2_T2" "hsc3_T2"
## [67] "hsc4_T2" "hsc5_T2"
## [69] "hsc6_T2" "hsc7_T2"
## [71] "hsc8_T2" "hsc9_T2"
## [73] "hsc10_T2" "hsc11_T2"
## [75] "hsc12_T2" "health1_T2"
## [77] "health2_T2" "health3_T2"
## [79] "health4_T2" "health5_T2"
## [81] "bis1r_T2" "bas1_T2"
## [83] "bas2_T2" "bas3_T2"
## [85] "bas4_T2" "bis2_T2"
## [87] "bas5_T2" "bas6_T2"
## [89] "bas7_T2" "bis3_T2"
## [91] "bas8_T2" "bas9_T2"
## [93] "bis4_T2" "bas10_T2"
## [95] "bis5_T2" "bas11_T2"
## [97] "bas12_T2" "bis6r_T2"
## [99] "bas13_T2" "bis7_T2"
## [101] "bis1_T2" "bis6_T2"
## [103] "eoe_mean_T1" "na.rm"
## [105] "eoe_mean_T2" "lst_mean_T1"
## [107] "lst_mean_T2" "aes_mean_T1"
## [109] "aes_mean_T2" "hsc_mean_T1"
## [111] "hsc_mean_T2" "health_mean_T1"
## [113] "health_mean_T2" "positive_mean_T1"
## [115] "negative_mean_T1" "environment_mean_T2"
## [117] "bis_mean_T2" "bas_mean_T2"
## [119] "environment_mean_afterEFA_T2" "LC"
## [121] "profile"
head(InputData) #先頭行確認
InputData <- InputData %>%
mutate(hsc.profile = recode(profile,
"1" = "medium",
"2" = "low",
"3" = "hsc"))
head(InputData)
discriptive_HSCS_by_profile <-
InputData %>%
dplyr::group_by(hsc.profile) %>% #性別でグルーピング
dplyr::summarise(n = n (), #グループの人数を出力
hsc_T1_mean = mean (hsc_mean_T1), #hsc_T1の平均
hsc_T1_sd = sd (hsc_mean_T1), #hcs_T1のSD
eoe_T1_mean = mean (eoe_mean_T1), #eoe_T1の平均
eoe_T1_sd = sd (eoe_mean_T1), #eoe_T1のSD
lst_T1_mean = mean (lst_mean_T1), #lst_T1の平均
lst_T1_sd = sd (lst_mean_T1), #lst_T1のSD
aes_T1_mean = mean (aes_mean_T1), #aes_T1の平均
aes_T1_sd = sd (aes_mean_T1), #aes_T1のSD
environment_mean = mean (environment_mean_afterEFA_T2), #environment_mean_afterEFA_T2の平均
environment_sd = sd (environment_mean_afterEFA_T2), #environment_mean_afterEFA_T2のSD
health_improvement = mean (LC), #精神的健康変化の平均
health_sd = sd (LC)) #精神的健康変化のSD
discriptive_HSCS_by_profile
InputData %>%
drop_na() %>% #欠損値をデータセットから除外
dplyr::group_by(hsc.profile) %>% #性別でグルーピング
dplyr::summarise(n = n (), #グループの人数を出力
environment_mean = mean (environment_mean_afterEFA_T2), #environment_mean_afterEFA_T2の平均
environment_sd = sd (environment_mean_afterEFA_T2)) #environment_mean_afterEFA_T2のSD
#データのグループ化
#hscのグループ
hsc.student <- InputData %>%
filter(hsc.profile == "hsc")
#低hscのグループ
low.student <- InputData %>%
filter(hsc.profile == "low")
#中hscのグループ
medium.student <- InputData %>%
filter(hsc.profile == "medium")
#相関係数
cor.test(hsc.student$environment_mean_afterEFA_T2, hsc.student$LC, method = "pearson") #HSCグループ
##
## Pearson's product-moment correlation
##
## data: hsc.student$environment_mean_afterEFA_T2 and hsc.student$LC
## t = 4.0461, df = 132, p-value = 8.816e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1722874 0.4750010
## sample estimates:
## cor
## 0.3321704
cor.test(low.student$environment_mean_afterEFA_T2, low.student$LC, method = "pearson") #低HSCグループ
##
## Pearson's product-moment correlation
##
## data: low.student$environment_mean_afterEFA_T2 and low.student$LC
## t = 0.88344, df = 41, p-value = 0.3821
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1706740 0.4197876
## sample estimates:
## cor
## 0.136676
cor.test(medium.student$environment_mean_afterEFA_T2, medium.student$LC, method = "pearson") #中HSCグループ
##
## Pearson's product-moment correlation
##
## data: medium.student$environment_mean_afterEFA_T2 and medium.student$LC
## t = 3.2977, df = 165, p-value = 0.001194
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1005990 0.3859526
## sample estimates:
## cor
## 0.2486637
lm.hsc <- lm(LC ~ environment_mean_afterEFA_T2, data = hsc.student)
summary(lm.hsc)
##
## Call:
## lm(formula = LC ~ environment_mean_afterEFA_T2, data = hsc.student)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6240 -0.4691 -0.1123 0.6180 2.4222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.93972 0.37478 -2.507 0.0134 *
## environment_mean_afterEFA_T2 0.32363 0.07999 4.046 8.82e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8983 on 132 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.1103, Adjusted R-squared: 0.1036
## F-statistic: 16.37 on 1 and 132 DF, p-value: 8.816e-05
confint(lm.hsc, level = 0.95)#95%CI
## 2.5 % 97.5 %
## (Intercept) -1.6810700 -0.1983751
## environment_mean_afterEFA_T2 0.1654119 0.4818558
lm.low <- lm(LC ~ environment_mean_afterEFA_T2, data = low.student)
summary(lm.low)
##
## Call:
## lm(formula = LC ~ environment_mean_afterEFA_T2, data = low.student)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.62455 -0.62053 -0.03509 0.63428 2.44931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3896 0.7556 -0.516 0.609
## environment_mean_afterEFA_T2 0.1416 0.1603 0.883 0.382
##
## Residual standard error: 1.046 on 41 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.01868, Adjusted R-squared: -0.005254
## F-statistic: 0.7805 on 1 and 41 DF, p-value: 0.3821
confint(lm.low, level = 0.95)#95%CI
## 2.5 % 97.5 %
## (Intercept) -1.9155792 1.1363355
## environment_mean_afterEFA_T2 -0.1821134 0.4653408
lm.medium <- lm(LC ~ environment_mean_afterEFA_T2, data = medium.student)
summary(lm.medium)
##
## Call:
## lm(formula = LC ~ environment_mean_afterEFA_T2, data = medium.student)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.96549 -0.48647 -0.05988 0.58358 2.87193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.92049 0.39895 -2.307 0.02228 *
## environment_mean_afterEFA_T2 0.29004 0.08795 3.298 0.00119 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8464 on 165 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.06183, Adjusted R-squared: 0.05615
## F-statistic: 10.87 on 1 and 165 DF, p-value: 0.001194
confint(lm.medium, level = 0.95)#95%CI
## 2.5 % 97.5 %
## (Intercept) -1.708195 -0.1327787
## environment_mean_afterEFA_T2 0.116383 0.4636898
png("figure/hsc_plot1.png", width = 600, height = 400)
plot1 <- ggplot(data = InputData, aes(x = environment_mean_afterEFA_T2, y = LC, group = factor(hsc.profile), colour = factor(hsc.profile))) +
geom_point() +
geom_smooth(method = "lm") +
theme_classic(base_size = 14, base_family = "serif") +
labs(y = "mental health improvement", colour = "profiles")
plot1 <- plot1 + labs(x = "perceived school environment change (1 = negative change, 4 = no change, 7 = positive change)") #1枚で作図
#メモ;xラベルがなぜか変化しないので、くどいけど2行で作図。これだと変化する。
print(plot1)
#dev.off()
#png("figure/hsc_plot2.png", width = 800, height = 400)
plot2 <- ggplot(data = InputData, aes(x = environment_mean_afterEFA_T2, y = LC)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~ hsc.profile) #並べて作図
plot2 <- plot2 + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
axis.text.x = element_text(size = 12),
legend.title = element_text(size = 9)) +labs(x = "perceived school environment change (1 = negative change, 4 = no change, 7 = positive change)",
y = "mental health improvement") #並べて作図
print(plot2)
#dev.off()
library(quantreg)
## Loading required package: SparseM
##
## Attaching package: 'SparseM'
## The following object is masked from 'package:base':
##
## backsolve
set.seed(1234) #乱数set
fit.hsc <- rq(LC ~ environment_mean_afterEFA_T2, data = hsc.student, tau = seq(0, 1, 0.25))
summary(fit.hsc, se = "boot", R = 2000, bsmethod= "wild")
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = hsc.student)
##
## tau: [1] 0
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -4.37086 0.00000 -Inf 0.00000
## environment_mean_afterEFA_T2 0.56278 0.00000 Inf 0.00000
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = hsc.student)
##
## tau: [1] 0.25
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -1.64059 0.55030 -2.98125 0.00342
## environment_mean_afterEFA_T2 0.36163 0.10694 3.38161 0.00095
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = hsc.student)
##
## tau: [1] 0.5
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -0.42385 0.46214 -0.91714 0.36074
## environment_mean_afterEFA_T2 0.19441 0.09404 2.06732 0.04066
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = hsc.student)
##
## tau: [1] 0.75
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -0.42581 0.62107 -0.68560 0.49416
## environment_mean_afterEFA_T2 0.34975 0.12876 2.71624 0.00749
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = hsc.student)
##
## tau: [1] 1
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 1.78577 0.00000 Inf 0.00000
## environment_mean_afterEFA_T2 0.24701 0.00000 Inf 0.00000
fit.low <- rq(LC ~ environment_mean_afterEFA_T2, data = low.student, tau = seq(0, 1 , 0.25))
summary(fit.low, se = "boot", R = 2000, bsmethod= "wild")
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = low.student)
##
## tau: [1] 0
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -5.01702 0.00000 -Inf 0.00000
## environment_mean_afterEFA_T2 0.73505 0.00000 Inf 0.00000
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = low.student)
##
## tau: [1] 0.25
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -2.00651 1.47776 -1.35780 0.18195
## environment_mean_afterEFA_T2 0.29497 0.27743 1.06326 0.29389
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = low.student)
##
## tau: [1] 0.5
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -0.51480 0.80362 -0.64060 0.52534
## environment_mean_afterEFA_T2 0.15547 0.17042 0.91230 0.36694
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = low.student)
##
## tau: [1] 0.75
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 0.97555 0.88651 1.10043 0.27756
## environment_mean_afterEFA_T2 -0.00425 0.17387 -0.02447 0.98060
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = low.student)
##
## tau: [1] 1
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 1.83570 0.00000 Inf 0.00000
## environment_mean_afterEFA_T2 0.19004 0.00000 Inf 0.00000
fit.medium <- rq(LC ~ environment_mean_afterEFA_T2, data = medium.student, tau = seq(0, 1 , 0.25))
summary(fit.medium, se = "boot", R = 2000, bsmethod= "wild")
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = medium.student)
##
## tau: [1] 0
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -8.81072 0.00000 -Inf 0.00000
## environment_mean_afterEFA_T2 1.41569 0.00000 Inf 0.00000
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = medium.student)
##
## tau: [1] 0.25
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -2.05810 0.40976 -5.02271 0.00000
## environment_mean_afterEFA_T2 0.43214 0.08146 5.30504 0.00000
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = medium.student)
##
## tau: [1] 0.5
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) -1.07526 0.45147 -2.38168 0.01837
## environment_mean_afterEFA_T2 0.31063 0.09510 3.26630 0.00133
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = medium.student)
##
## tau: [1] 0.75
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 0.28294 0.59740 0.47361 0.63640
## environment_mean_afterEFA_T2 0.15206 0.12696 1.19765 0.23277
##
## Call: rq(formula = LC ~ environment_mean_afterEFA_T2, tau = seq(0,
## 1, 0.25), data = medium.student)
##
## tau: [1] 1
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 5.78011 0.00000 Inf 0.00000
## environment_mean_afterEFA_T2 -0.61083 0.00000 -Inf 0.00000
ステップ1で「性別」、ステップ2で「知覚された学校環境変化」「HSCS」、ステップ3で「知覚された学校環境変化×HSCS」を独立変数として順に投入。独立変数は「性別」以外、中心化。HSCSは1時点目の得点を用いる。従属変数はwell-beingの潜在差得点。 参考資料として:chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/http://cogpsy.educ.kyoto-u.ac.jp/personal/Kusumi/datasem13/shinya2.pdf
dat <- InputData %>% drop_na() %>% select_("hsc_mean_T1", "environment_mean_afterEFA_T2", "LC", "child_gender_T1") #na削除したうえで必要な変数抽出
hscs_c <- dat$hsc_mean_T1 - mean(dat$hsc_mean_T1) #HSCSの中心化
env_c <- dat$environment_mean_afterEFA_T2 - mean(dat$environment_mean_afterEFA_T2) #知覚された学校環境変化の中心化
wellbeing <- dat$LC #well-beingの潜在差得点の名前変更
gender <- dat$child_gender_T1 #性別T1の変数名変更
cor(data.frame(dat$child_gender_T1, dat$hsc_mean_T1, dat$environment_mean_afterEFA_T2, dat$LC))#中心化前の変数
## dat.child_gender_T1 dat.hsc_mean_T1
## dat.child_gender_T1 1.00000000 0.16152287
## dat.hsc_mean_T1 0.16152287 1.00000000
## dat.environment_mean_afterEFA_T2 -0.09337277 0.02003445
## dat.LC -0.00936455 0.10232572
## dat.environment_mean_afterEFA_T2
## dat.child_gender_T1 -0.09337277
## dat.hsc_mean_T1 0.02003445
## dat.environment_mean_afterEFA_T2 1.00000000
## dat.LC 0.25558481
## dat.LC
## dat.child_gender_T1 -0.00936455
## dat.hsc_mean_T1 0.10232572
## dat.environment_mean_afterEFA_T2 0.25558481
## dat.LC 1.00000000
cor(data.frame(gender, hscs_c, env_c, wellbeing))
## gender hscs_c env_c wellbeing
## gender 1.00000000 0.16152287 -0.09337277 -0.00936455
## hscs_c 0.16152287 1.00000000 0.02003445 0.10232572
## env_c -0.09337277 0.02003445 1.00000000 0.25558481
## wellbeing -0.00936455 0.10232572 0.25558481 1.00000000
「性別」だけ投入。結果メモ:モデルは非有意
reg1 <- lm(wellbeing ~ gender)
summary(reg1)
##
## Call:
## lm(formula = wellbeing ~ gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.10890 -0.53642 -0.00724 0.56920 2.67759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43584 0.07620 5.72 2.63e-08 ***
## gender -0.01724 0.10758 -0.16 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9238 on 293 degrees of freedom
## Multiple R-squared: 8.769e-05, Adjusted R-squared: -0.003325
## F-statistic: 0.0257 on 1 and 293 DF, p-value: 0.8728
「知覚された学校環境変化」と「HSCS」を追加投入。結果メモ:モデルは有意。学校環境が正の効果。
reg2 <- lm(wellbeing ~ gender + env_c + hscs_c)
summary(reg2)
##
## Call:
## lm(formula = wellbeing ~ gender + env_c + hscs_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.90240 -0.49893 -0.08848 0.62892 2.57619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.428522 0.074221 5.774 1.99e-08 ***
## gender -0.002652 0.105725 -0.025 0.9800
## env_c 0.263496 0.058906 4.473 1.11e-05 ***
## hscs_c 0.112978 0.066263 1.705 0.0893 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8917 on 291 degrees of freedom
## Multiple R-squared: 0.07478, Adjusted R-squared: 0.06524
## F-statistic: 7.84 on 3 and 291 DF, p-value: 4.764e-05
anova(reg1, reg2) #R^2の増加量の検定
「知覚された学校環境変化」と「HSCS」の交互作用項を追加投入。結果メモ:モデルは非有意。交互作用項も非有意。
reg3 <- lm(wellbeing ~ gender + env_c + hscs_c + env_c:hscs_c)
summary(reg3)
##
## Call:
## lm(formula = wellbeing ~ gender + env_c + hscs_c + env_c:hscs_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.90460 -0.50746 -0.09731 0.62458 2.42529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4263640 0.0742011 5.746 2.31e-08 ***
## gender -0.0008841 0.1056737 -0.008 0.9933
## env_c 0.2590318 0.0589968 4.391 1.59e-05 ***
## hscs_c 0.1242824 0.0669389 1.857 0.0644 .
## env_c:hscs_c 0.0902023 0.0778360 1.159 0.2475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8912 on 290 degrees of freedom
## Multiple R-squared: 0.07904, Adjusted R-squared: 0.06634
## F-statistic: 6.222 on 4 and 290 DF, p-value: 8.133e-05
anova(reg2, reg3) #R^2の増加量の検定
交互作用項は有意ではなかったが、一応単純傾斜検定してみる
hscs_h <- dat$hsc_mean_T1 - (mean(dat$hsc_mean_T1, na.rm = TRUE) + sd(dat$hsc_mean_T1, na.rm = TRUE)) #HSCS +1SD
hscs_l <- dat$hsc_mean_T1 - (mean(dat$hsc_mean_T1, na.rm = TRUE) - sd(dat$hsc_mean_T1, na.rm = TRUE)) #HSCS -1SD
結果の出力は、env_cをみる。結果メモ:env_cは有意。後述の-1SDより効果高いようにみえる。
result_h <- lm(wellbeing ~ gender + env_c + hscs_h + env_c:hscs_h)
summary(result_h)
##
## Call:
## lm(formula = wellbeing ~ gender + env_c + hscs_h + env_c:hscs_h)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.90460 -0.50746 -0.09731 0.62458 2.42529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5252657 0.0960811 5.467 9.88e-08 ***
## gender -0.0008841 0.1056737 -0.008 0.9933
## env_c 0.3308132 0.0827044 4.000 8.05e-05 ***
## hscs_h 0.1242824 0.0669389 1.857 0.0644 .
## env_c:hscs_h 0.0902023 0.0778360 1.159 0.2475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8912 on 290 degrees of freedom
## Multiple R-squared: 0.07904, Adjusted R-squared: 0.06634
## F-statistic: 6.222 on 4 and 290 DF, p-value: 8.133e-05
結果の出力は、env_cをみる。結果メモ:env_cは有意。効果は+1SDより小さめ。
result_l <- lm(wellbeing ~ gender + env_c + hscs_l + env_c:hscs_l)
summary(result_l)
##
## Call:
## lm(formula = wellbeing ~ gender + env_c + hscs_l + env_c:hscs_l)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.90460 -0.50746 -0.09731 0.62458 2.42529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3274622 0.0863434 3.793 0.000181 ***
## gender -0.0008841 0.1056737 -0.008 0.993331
## env_c 0.1872504 0.0882865 2.121 0.034775 *
## hscs_l 0.1242824 0.0669389 1.857 0.064374 .
## env_c:hscs_l 0.0902023 0.0778360 1.159 0.247460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8912 on 290 degrees of freedom
## Multiple R-squared: 0.07904, Adjusted R-squared: 0.06634
## F-statistic: 6.222 on 4 and 290 DF, p-value: 8.133e-05
参考資料としてhttps://www.slideshare.net/makotohirakawa3/mra-42251907
library(pequod)
## Loading required package: car
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
model <- lmres(LC ~ child_gender_T1 + environment_mean_afterEFA_T2 + hsc_mean_T1 + environment_mean_afterEFA_T2:hsc_mean_T1, centered = c("environment_mean_afterEFA_T2", "hsc_mean_T1"), data = dat) #交互作用の検討
summary(model)
## Formula:
## LC ~ child_gender_T1 + environment_mean_afterEFA_T2 + hsc_mean_T1 +
## environment_mean_afterEFA_T2.XX.hsc_mean_T1
## <environment: 0x000000001fad2c10>
##
## Models
## R R^2 Adj. R^2 F df1 df2 p.value
## Model 0.2811 0.0790 0.0663 6.2225 4.0000 290 8.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residuals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.9046 -0.5075 -0.0973 0.0000 0.6246 2.4253
##
## Coefficients
## Estimate StdErr t.value
## (Intercept) 0.42636 0.07420 5.74606
## child_gender_T1 -0.00088 0.10567 -0.00837
## environment_mean_afterEFA_T2 0.25903 0.05900 4.39061
## hsc_mean_T1 0.12428 0.06694 1.85665
## environment_mean_afterEFA_T2.XX.hsc_mean_T1 0.09020 0.07784 1.15888
## beta p.value
## (Intercept) < 2e-16 ***
## child_gender_T1 -0.0005 0.99333
## environment_mean_afterEFA_T2 0.2492 2e-05 ***
## hsc_mean_T1 0.1072 0.06437 .
## environment_mean_afterEFA_T2.XX.hsc_mean_T1 0.0662 0.24746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Collinearity
## VIF Tolerance
## child_gender_T1 1.0369 0.9644
## environment_mean_afterEFA_T2 1.0144 0.9858
## hsc_mean_T1 1.0504 0.9520
## environment_mean_afterEFA_T2.XX.hsc_mean_T1 1.0273 0.9734
model_ss <- simpleSlope(model, pred ="environment_mean_afterEFA_T2", mod1 = "hsc_mean_T1")
summary(model_ss)
##
## ** Estimated points of LC **
##
## Low environment_mean_afterEFA_T2 (-1 SD)
## Low hsc_mean_T1 (-1 SD) 0.1613
## High hsc_mean_T1 (+1 SD) 0.2317
## High environment_mean_afterEFA_T2 (+1 SD)
## Low hsc_mean_T1 (-1 SD) 0.4936
## High hsc_mean_T1 (+1 SD) 0.8188
##
##
##
## ** Simple Slopes analysis ( df= 290 ) **
##
## simple slope standard error t-value p.value
## Low hsc_mean_T1 (-1 SD) 0.1873 0.0883 2.12 0.0348 *
## High hsc_mean_T1 (+1 SD) 0.3308 0.0827 4.00 0.0001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## ** Bauer & Curran 95% CI **
##
## lower CI upper CI
## hsc_mean_T1 -0.8648 4.104
PlotSlope(model_ss) #作図